e-Learning, e-Education, and Online Training

The rapid growth of network teaching resources brings more learning opportunities for people, but also makes it more and more expensive for users to find the resources they need, and users often get lost in a large number of resources. In view of the problem of low coverage of existing teaching resource recommendation methods, this paper studies a resource recommendation method of online ideological and political courses based on content correlation. In this method, word frequency-inverse document matrix (TF-IDF) is used to mine the keywords of ideological and political teaching resources, and the user interest description matrix is established, and the content correlation degree is calculated by cosine similarity. Top-k online teaching resources of ideological and political courses in universities that are most similar to the target customer’s interest description vector are selected based on the correlation degree and recommended to the user. The results show that the accuracy rate and coverage rate of the proposed method are higher than those of the collaborative filtering and utility-based methods, indicating that the proposed method has better performance.

In the process of resource transmission, the existing multi-interactive sharing system does not take encryption measures, which leads to poor security and incomplete information transmission. Therefore, focusing on the teaching resources of electric power discipline, the optimization of multi-interactive sharing system is realized from three aspects: hardware, database and software. Improve the communication network structure, modify the teaching course resource collector, microprocessor, resource memory, collector and other equipment components, and complete the hardware system optimization through circuit modification and connection. A systematic database of teaching resources of electric power specialty is established. Constrained by the multi-interactive sharing protocol, the course resources of electric power discipline are compressed and added to the transmission queue by data encryption to complete the uploading task. After searching and downloading resources, the system finally realized multiple interactive sharing functions. Through the system test experiment, the loss rate of shared packets is less than 2% of the total resources, and the sharing time is less than 8000 ms.

The existing online higher education resource depth mining methods do not segment transactions, resulting in a long time of resource mining. In order to mine resources more quickly, an in-depth mining method of online higher education resources based on K-means clustering is designed. The cloud computing framework MapReduce is used as the operation framework of mining algorithm, and Weka is used as the data mining tool; Preprocess the data and segment the transactions in data mining to reduce its granularity and facilitate the mining; Based on K-means clustering, an online higher education resource mining algorithm is designed to realize the in-depth mining of online higher education resources. The test results show that the SSE, DB and XB values of the design method are small, and the s values are large; The average running time of 10, 20 and 50 runs on each data set is short; After adding 3 gb dirty data, the clustering accuracy is still high, which shows that the design method has good mining performance, can improve the efficiency of mining resources, promote the innovation and development of education and industry, and realize the deepening application of big data in education.

The traditional online education resource recommendation system for international finance courses has high recommendation error and poor accuracy. Therefore, this paper designs an online education resource recommendation system for international finance courses based on preference data collection. Obtain the online education resources of international finance courses according to the preference data collection method, mine the user's interest points from the user's behavior, build the user model module, realize the online education resources search of international finance courses through the search engine module, and take the IBM small server as the server of the online education resources recommendation system to effectively realize the online education resources recommendation of international finance courses. The experimental results show that the average absolute error of the design system is low, and has good recommended quality and stability, which can meet the initial design requirements of the system.

The conventional agricultural online educational resources intelligent sharing platform has the problem of unclear educational resources storage architecture, resulting in poor response performance of the sharing platform. Therefore, an agricultural online educational resources intelligent sharing platform based on blockchain technology is designed. Hardware part: select DSP as the processor, and then generate serial port, interrupt, PWM wave and other functions to the external port according to the instructions; Software part: build an agricultural online education database, fully tap the potential of information-based teaching, optimize the storage architecture of educational resources with blockchain technology, use smart contracts to encode and operate data, set permissions according to the type of users, and design the software data migration function of intelligent sharing platform. Experimental results: the average response time between the agricultural online educational resources intelligent sharing platform and the other two sharing platforms is 103.37 ms, 138.51 ms and 140.64 ms, indicating that the performance of the agricultural online educational resources intelligent sharing platform integrated with blockchain technology is more perfect.

The course recommendation algorithm fails to consider the impact of semantic correlation between course keywords on feature similarity calculation when modeling, resulting in low recommendation accuracy. An economic management course recommendation algorithm in intelligent education cloud platform is proposed. The text of economic management course is collected by web crawler technology, and the words are extracted by text word segmentation, removal of stop words and other preprocessing. Based on the word2vec model, the text word vector of economic management course is established to measure the similarity between words. The word vector clustering and TF-IDF word weight are used to represent the course features. Design the course recommendation algorithm, calculate the similarity between courses, and generate the course similarity matrix for course recommendation. The experimental results show that the recommendation accuracy of the economic management course recommendation algorithm designed in this paper is higher than 0.9% in the three aspects of course name, course overview and course objectives, and the maximum recommendation time is 5min, which has a good application effect.

In the process of grammatical topic recommendation, affected by the dimension of word vector, there is a problem of low recommendation accuracy. To solve this problem, a deep recommendation method of grammar topics for English online teaching is designed. Based on the classification theory of learning outcomes, the dimensions of students’ learning styles in English online classrooms were measured. It can accurately identify the characteristics of English grammar teaching and stimulate students’ interest in learning. The DeepFM model is constructed by using English online teaching technology, the parallel parameters are optimized, and the deep recommendation mode of grammar topics is set. Experimental results: The accuracy rates of the proposed method for in-depth recommendation of grammar topics for English online teaching and the other two methods for in-depth recommendation of grammar topics are: 73.183%, 63.205%, and 63.258%, respectively. The experimental results show that the accuracy of the in-depth recommendation method of grammar topics has been improved after fully combining the English online teaching technology.

The existing college art education course resource allocation system has the problem that the course resource allocation process is too cumbersome, resulting in too long network delay. A kind of college art education course resource allocation system based on virtual grid is designed. Hardware part: use an externally provided 1 GHz sampling clock to convert the communication channel analog-to-digital; software part: extract the performance characteristics of art education courses, change the identities of production subjects and dissemination subjects, enhance students’ right to choose courses independently, and optimize course resource allocation process, Utilize the virtual grid design system node deployment function. Experimental results: The mean network delays of the resource allocation system for college art education courses and the other two systems are: 48.483 ms, 63.886 ms, and 64.842 ms, respectively, which shows that after integrating the virtual grid algorithm, the designed college art education courses The resource allocation system has better performance.

The existing resource database input information control methods have the problem of complex data set block integration process, resulting in low control rate. Therefore, a data mining based intelligent education resource database input information control method is proposed. Identify the performance characteristics of intelligent education resource database, adopt a comprehensive and open architecture, block data with file granularity, optimize the data set block integration process, set the security range by using data mining, and design the input information control mode according to the needs of teaching and learning. Experimental results: the average control value of the input information control method of the intelligent education resource database is 69.388%, which shows that after fully combining the data mining technology, the proposed method is more effective and more efficient.

In view of the poor effect of courseware display in the current multimedia courseware display system, a multimedia courseware synchronous display system for distance teaching is designed. The component library is constructed by SQL Server 2000, and the navigation interface based on distance teaching is designed to reflect the friendliness of multimedia interactive system. Use the visual card editor to present the visual integration environment. The edited metadata file is transmitted to the multimedia of distance teaching through the editing subsystem. The multi-level virtual memory is used to control the remote access process through the unique identification of multimedia files. Calculate the maximum and minimum values in the information base, and judge whether the information is abnormal point information based on this. Use the information extraction method to edit the distance teaching courseware. Design the sending and receiving process of multimedia courseware information for complete transmission of information. Through the synchronous display function of courseware, it can be broadcast on demand by students. The experimental results show that the number of downloads of relevant theoretical articles, teaching courseware downloads and relevant website clicks of the system users are within the range of actual downloads, with an error of 0, and has a good display effect.

In the social environment with the rapid development of information technology, the importance of educational technology in the field of education is becoming more and more prominent. Traditional music teaching methods limit the openness and extensibility of music classrooms to a certain extent, and have been unable to meet the current needs of big data. Therefore, it is necessary to design a new music resource sharing system based on the mobile terminal. The hardware part designs the MAC resource controller and the data controller, and the software part first designs the music teaching resource sharing architecture. Secondly, the music teaching resource sharing module is designed based on the mobile terminal, so as to realize the music teaching resource sharing. The system test results show that the designed music teaching resource sharing system has good performance and certain application value, which can be used as a reference for subsequent music teaching.

Traditional Chinese education resource sharing system has the problem of slow sharing speed. Therefore, this paper designs a remote sharing system of Chinese education resource based on information fusion. Hardware parts in system, we design a microcontroller, USB (CH376) extension module, communication module, keyboard interface circuit and LCD display circuit, the system software part, the design of system functions, the system resources are collected, and USES the information fusion method of resource integration, and puts forward the remote resources sharing process, To realize the remote sharing of Chinese education resources based on information fusion. The experimental results show that the researched system has fast sharing speed under single information query and multi-concurrent user query, and the accuracy of information query and resource sharing are both high.

In order to improve the integrated storage capacity and sharing security of educational information resources, this paper proposes and designs an intelligent educational information resources integration and sharing system based on cloud computing. The system hardware is composed of user management module, education resource management module, related education resource system integration module and system management module. The curriculum resource management module is designed with b/s architecture, namely browser/server structure. In short, the b/s structure can be divided into three levels: client software, application server, database server, etc. Through user management, educational resource management and related educational resource system integration, software research is realized. The experimental results show that the intelligent education information resources integration and sharing system based on cloud computing can effectively improve the storage capacity and the security of sharing.

The explosive growth of the number and scale of online education resources makes it difficult for learners of elderly care to obtain the online teaching resources they need in time. However, the traditional resource collaborative filtering recommendation method has the disadvantage of low recommendation accuracy. To solve this problem, this study proposed a recommendation method for collaborative filtering of online teaching resources in elderly care. The organizational form of online teaching resources for elderly care major was deeply analyzed, and then the learning behavior data of learners were collected and analyzed, and the preferences of target learners were calculated. Based on this, the BP neural network is used to construct the target learner preference model, and then the collaborative filtering algorithm is used to predict the score of online teaching resources, so as to realize the recommendation of collaborative filtering of online teaching resources. Experimental data show that compared with traditional methods, the proposed method has higher recommendation accuracy and recall rate, which proves the effectiveness of the proposed method.

In view of the poor effect of teaching information recommendation, a new personalized recommendation system of Ideological and political online education resources is studied under the support of artificial intelligence technology. The system hardware includes content recommendation module, project collaborative filtering recommendation module and user collaborative filtering recommendation module. In the system software part, the user’s interest is calculated and the recommendation result is generated based on the browsing and visiting record data of the system user. The system performance test results show that the recommended performance of the designed system is significantly improved and can fully meet the use requirements.

The balanced allocation of resources in network teaching can effectively improve the utilization of educational resources. Aiming at the poor effect of balanced allocation of ideological and political education resources in colleges and universities, this paper proposes a method based on big data clustering. This research has constructed the university thought politics network teaching resources database, realizes the teaching information collection and the classified storage. The large data clustering algorithm is applied to teaching resource allocation management. It optimizes the management model of ideological and political network teaching resources. Finally, the experimental results show that this method has high practicability and high efficiency in information allocation. This method can optimize the allocation structure under the condition that the total input of resource construction is relatively constant.

In view of the poor effect of the current balanced allocation of online education resources, this study proposes a balanced allocation model of online education resources of economics and management in colleges and universities based on parallel clustering. Firstly, combined with the principle of parallel clustering, an online education resource database is constructed to collect, analyze and store a large number of economic management teaching resources. Then build the information management platform and optimize the evaluation index of balanced allocation of educational resources. Finally, the experiment proves that the model in this paper has high practicability in the practical application process and fully meets the research requirements.

Due to the diversity of online English teaching platform data, the accuracy of its classification results is low. Therefore, a data classification method of online English teaching platform based on deep neural network is proposed. In order to reduce the difference attribute between the data, the data of online English teaching platform are normalized. On this basis, the attributes are discretized, the corresponding attribute codes are established, and two constraints are constructed. The deep neural network is used to classify the data of online English teaching platform. The test results show that the accuracy of data classification can reach more than 98.0%.

The traditional online education system data query method has the problem of unclear track data type, which leads to a long query time. To solve this problem, this study designs a data optimization query method of online education system based on decision tree. According to the teaching objectives of teachers and the characteristics of courses, extract the course characteristics of online education system, and adjust the data segmentation process, so as to extract the grid memory query index structure. Then select a field as the segmentation basis and mark, and use the decision tree to identify the trajectory data type. Finally, set the data optimization query mode by continuously predefined query interval. The experimental results show that compared with the other two query methods, the query time of this method is less, which shows that the application performance of the data optimization query method of online education system integrated with decision tree is better.

Improving the quality of resource integration is one of the effective ways to improve the utilization of teaching resources. Therefore, aiming at the online mathematics course, this research uses the deep neural network as the basic means to carry out a new design of the teaching resource integration model. First, the overall design is carried out, including the model architecture, resource data transmission mode, data management mode, model function design and cloud platform configuration. Then, using the elastic computing technology in the field of deep neural network technology, based on the collection of relevant resources, the cloud storage and management of resources are implemented with the mathematics course courseware as the core and the extensible software as the support. Finally, in the deep neural network, the dynamically expandable and virtualized storage resources are used to provide the storage and access services of teaching resources, and the integration effect is improved by classifying the resource categories when uploading resources. Compared with the traditional model, the results show that the characteristics of this model are more prominent, and the application advantages in accuracy and resource reading speed are obvious.

At present, the educational resources of the online education platform are mixed, and the matching accuracy of the resource matching method of the platform is low. This paper designs and realizes the resource matching method of online education platform based on artificial intelligence technology. The information is rectified by histogram equalization, and the multi-dimension feature is sorted according to the Euclidean distance ratio method, then the multi-dimension registration model is constructed. The edge of multi-dimensional image registration is precisely processed, and finally individualized matching of resources can be realized. The experimental results show that the resource matching method based on AI technology can make students and teachers communicate and communicate with each other in time, and make full use of the teaching resources. This system simple operation and the pointed teaching design, lets the student not be subjected to the time and place control, the choice likes the teaching content, provides the service for the education teaching activity.

The traditional long-distance sharing method of teaching resources is insufficient in the analysis of resources, and it is difficult to achieve a clear classification, which leads to the low integrity of resource collection and transmission. According to the functional characteristics of the curriculum resources, adjust the screening process of music teaching resources, collect and sort out folk music materials, use big data analysis technology to build open teaching mode, and design a remote sharing method. The results show that the transmission integrity of the remote sharing method is 66.990%, 57.450% and 58.190% respectively, which shows that the remote sharing method has better performance.

Online financial education resources are massive. Traditional algorithms are affected by the load of resource processing nodes, resulting in low accuracy and long mining time. Therefore, a massive online financial education resource mining algorithm based on Apriori TIDs is proposed. The Apriori TIDS algorithm is used to establish the characteristic equation of massive online financial education resources. By extracting the number of principal factors of the characteristic vector, the proportion of financial education resources in each dimension of each resource processing node in its total resources is calculated, so as to obtain the residual value of financial education resources of the resource processing node, and calculate the dynamic weight of financial education resources based on this, The residual load capacity of resource processing nodes is obtained. Combined with the design of massive online financial education resource mining algorithm, the mining of massive online financial education resources is realized. Experimental results show that the proposed algorithm can not only improve the accuracy of mining, but also shorten the mining time and have better mining performance.

Due to the long response time of the traditional financial management education resource sharing system, a financial management education resource sharing system based on data mining is designed. Firstly, the hardware configuration structure and function of the financial management education resource sharing system are optimized to improve the hardware performance of the system. Then, the massive financial management education resources are effectively stored and classified, and the targeted real-time extraction and sharing of teaching information are carried out. Finally, the experiment proves that the response time of the educational resource sharing system of financial management specialty based on data mining is short, and it has high practicability in the process of practical application.

In view of the current use of the linkage university student employment service platform, the recommendation method under the “double innovation” education perspective is generally based on the stand-alone mode, with limited processing capabilities and scalability, resulting in low system recommendation accuracy and recall rates. In order to solve this problem, a personalized recommendation system for college students’ employment education resources based on a cloud platform is designed. Use the Elastic Search open source search engine to build indexes so that users can quickly locate the resources they need from the search results. Build a talent training decision-making system platform to enable students to choose high-quality module resources that they are interested in. Design a hypertext service platform to obtain recommended paths. Establish a keyword index platform to index educational resources based on keywords. Construct a vector space model, describe the keyword vector, and determine the similarity between the relevance of the courseware and the category. Analyze queries and documents, remove stop words, extract stems, and vectorize documents. The document content is represented by the feature weight set, and a personalized recommendation process is designed. It can be seen from the experimental results that the highest recommendation accuracy rate of the system is 99%, and the highest recall rate is 98%, which has an efficient recommendation effect.

Aiming at the problem that the low matching degree between educational resource path planning and learners’ needs affects the success rate of recommendation, a distance education resource optimization recommendation method based on particle swarm optimization algorithm is proposed. Starting with the attributes of users and distance education resources, a portrait model is established to accurately describe the characteristics of users and resources. The characteristics of users and distance education resources in personalized learning resources are parameterized, the path of education resources is planned based on particle swarm optimization algorithm, and the learning path suitable for learners is formed by the reorganization and sorting of resources. A learner neighbor is established, and a recommendation model is established according to the difference matching degree between learners and learning resources. The experimental results show that when the number of educational resources is 1000, the average success rate of Distance Education Resource Recommendation Method Based on particle swarm optimization algorithm is 87.1%, which is 8.3% and 6.9% higher than that based on multivariate hybrid criteria fuzzy algorithm and multi-layer perceptron model. Therefore, the recommendation success rate of this method is relatively the highest, and can provide a set of learning resources that better match the characteristics of learners.

In order to solve the problems of low prediction accuracy and long prediction time of traditional resource demand prediction methods, an online learning resource demand prediction method based on BP neural network is proposed. Online learning resources are collected, online learning resource management evaluation indicators are constructed, and online learning resource demand prediction algorithms are optimized. Finally, experiments show that the resource demand prediction accuracy of this method is higher than that of traditional methods, it can fully meet the requirements of online learning resource demand forecasting, and can help improve the efficiency and quality of online learning.

In order to solve the problem that the evaluation results of the ideological and political effect of the Internet of things network course are not accurate, this paper puts forward the research on the evaluation method of Ideological and political effect of online courses of Internet of things. The influencing factors of Ideological and political effect of online courses are deeply analyzed, and the evaluation indexes of Ideological and political effect of online courses are determined by KMO statistics and factor analysis; The analytic hierarchy process is applied to calculate the weight of evaluation indexes and construct the evaluation model of Ideological and political effect of online courses; Formulate the evaluation standard of Ideological and political effect of online courses, so as to realize the evaluation of Ideological and political effect of online courses. The experimental results show that compared with the existing methods, the minimum delay determined by the evaluation index obtained by the proposed method is 0.8s, the minimum delay calculated by the index weight is 2.3 s, the gap between the evaluation result and the actual evaluation value is less than 1 point, and the accuracy of the evaluation result is high. The above data fully confirm the feasibility and effectiveness of the proposed method.

China adheres to the core of “cultivating college students’ innovation and entrepreneurship ability” and attaches great importance to innovation and entrepreneurship education. On the basis of establishing the evaluation index system of classroom teaching quality, this paper establishes the evaluation model of classroom teaching quality based on BP neural network. The results were validated using the Matlab Neural Network Toolkit, and the collected data was used for network training, optimization, and testing. It shows that the model can be used to evaluate the quality of classroom teaching, and can make scientific and reasonable decisions according to the evaluation indicators.

In order to solve the shortcomings of low accuracy and long time-consuming in the evaluation process of traditional teaching effect evaluation methods, this paper applies the decision tree algorithm to this, and designs an evaluation method for the teaching effect of oral English teaching. Different from other methods, the method designed in this paper is to evaluate students and teachers. After the evaluation system is constructed, the decision tree algorithm is used to process the collected original data samples, and the data attribute classification rules are set, the evaluation score is formulated, and the final evaluation is obtained by synthesizing the evaluation results. The experiment proves that the designed evaluation method has high accuracy and short time-consuming, which proves that the designed evaluation method has good evaluation effect.

In view of the low accuracy of the traditional methods in the evaluation of the quality of accounting online education, artificial intelligence technology is used to study the evaluation methods of the quality of accounting online education. The dgk means algorithm is used to mine the education quality evaluation index data. According to the mining results, the tndicator unit is constructed under the basic principles of the evaluation system, the evaluation index is obtained, the correlation coefficient of the evaluation index is calculated, and the online education quality evaluation is carried out through the established BP evaluation neural network. Simulation experiments show that the proposed method has high accuracy and good evaluation effect.

The ability of independent innovation in the field of artificial intelligence is a key element to occupy the commanding heights of future technology and talent competition in China. The cultivation of artificial intelligence talents, especially the cultivation high-end talent, it is essential to promote the development of artificial intelligence industry. At present, the market for artificial intelligence talents is in great demand. There is a shortage of high-end artificial intelligence talents, the weak strength of artificial intelligence teachers in universities, and the incomplete education system for talent training and many other issues. This paper combines some measures taken by Hunan Normal University for graduate students in the field of artificial intelligence in the degree of electronic information, the necessity and feasibility of artificial intelligence training senior talents are discussed for social needs in the field of artificial intelligence, The training goals and methods of postgraduate students in the field of artificial intelligence under the professional category of electronic information major are clarified, and the construction of artificial intelligence field is planned.

China’s independent innovation ability in the field of artificial intelligence is a key link to occupy the commanding heights of future science and technology and talent competition. The cultivation of artificial intelligent talents is crucial to promote the development of the artificial intelligent industry. There is a large demand for artificial intelligence talents in the market, but the development of artificial intelligence in colleges and universities is still faced with many problems, such as the construction of related disciplines and majors has just started, the strength of artificial intelligence teachers in colleges and universities is weak, the talent training is mainly based on postgraduate education and the education system is not perfect. This paper analyzes the current cultivation situation of artificial intelligence major in undergraduate colleges and vocational colleges, points out the shortcomings in the construction of artificial intelligence major, and gives some suggestions.

In order to solve the problem of high leak and error rate in the process of firmware security verification of distance learning terminal, a method of firmware security verification of distance learning terminal based on MD5 algorithm is proposed in order to solve the adaptability problem of existing security verification methods. According to the structure of distance learning terminal, the corresponding structure model is constructed, and the firmware types of distance learning terminal are divided under this model. Collect different types of firmware programs, use MD5 algorithm to verify user identity, detect firmware code security defects and malicious attacks, and identify firmware service daemon. Combined with the verification results of user identity, the final firmware security verification results are obtained through the comparison of attacks and firmware guard strength. Through the test experiment, it is concluded that the average missed acceptance rate and false acceptance rate of the design firmware safety verification method are less than 1%, and are suitable for multiple firmware samples, which proves the safety verification effect and adaptability of the design method.

In order to solve the problems of low warning accuracy and long warning time in the existing teaching quality early warning system, this paper presents a computerized accounting teaching data quality early warning system based on artificial intelligence. In the hardware part of the system, the data acquisition module, storage module, processor module and LCD display module are mainly designed. In the software part of the system, the data are collected and cleaned in advance, then the data association relationship is mined, and the early warning method is put forward, so as to realize the early warning of computerized accounting teaching data quality based on artificial intelligence. The experimental results show that the proposed early warning system effectively improves the accuracy and efficiency of early warning.

Because the traditional substation battery performance evaluation model has some problems, such as the fuzzy structural characteristics of the substation battery, resulting in excessive corrosion rate, this paper designs a new substation battery performance evaluation model based on the new series parallel topology. Obtain the performance parameters of the battery pack, predict the operation performance and failure of the battery, calculate the relationship function between open circuit voltage and electrolyte density, identify the structural characteristics of the battery pack in the substation, optimize the voltage regulation strategy of the high-frequency scheme, charge according to the specified constant current, calculate the amount of electricity released by the battery in the discharge process, and build a performance evaluation model with a new series parallel topology. The experimental results show that, compared with the other two traditional evaluation models, the corrosion rate of the substation battery performance evaluation model proposed in this paper is the lowest, with an average value of 3.269, which shows that the substation battery performance evaluation model described in this paper has better performance and certain effectiveness.

The traditional vocal music teaching model has been unable to meet the needs of today’s vocal music teaching, and the promotion and improvement of the online vocal music teaching model is imperative. Due to the influence of various factors (online equipment, environmental noise, etc.), there are certain defects in the timbre of online vocal music teaching, which cannot guarantee the effect of vocal music teaching. Preprocess the timbre signal of online vocal music teaching (remove mute segment, pre-emphasis and windowing), and based on this, extract timbre signal features (time domain feature, frequency domain feature and cepstral domain feature). In this paper, a feature comparison model of timbre signal is constructed to reduce the dimension of timbre signal feature vector. It adopts SAGA algorithm to determine the timbre evaluation formula of online vocal music teaching, and realizes the evaluation of timbre of online vocal music teaching. The experimental data show that compared with the reference standard, the complete rate of timbre signal feature extraction and the correct rate of timbre evaluation obtained by the proposed method are higher. The experimental results fully confirm the effectiveness and feasibility of the proposed method.

In order to solve the problem of low evaluation accuracy and efficiency in the existing teaching quality evaluation methods, this paper proposes and constructs a new teaching quality evaluation model of hotel management specialty based on association rules. Set SERVQUAL Association evaluation objective, design two-way evaluation structure, and establish G1 internal association weight evaluation system. On this basis, the entropy method is used to complete the construction of the related teaching quality evaluation model. The final model test results show that: compared with the initial teaching evaluation model, the evaluation accuracy of the teaching quality evaluation model under the association rules designed in this paper is higher, indicating that it has practical application value and social significance.

Aiming at the problem of poor evaluation effect of traditional teacher performance evaluation system, this study designs a new teacher performance evaluation system of higher vocational colleges based on fuzzy comprehensive evaluation model. In the hardware part, B/S manager and ASP.NET processor are designed to help the system better adapt to the wan environment, and implement the operation of application program based on Web type dynamic server. In the software part, on the basis of analyzing the demand of performance evaluation of teachers in higher vocational colleges, the fuzzy comprehensive evaluation model of performance evaluation is constructed, and the functional module and database module are designed. The test results show that the application performance of the system is good.

With the increase of distance learning data, the traditional methods have some problems, such as low efficiency and poor accuracy. Therefore, this paper studies the real-time collection method of learning state data of distance education based on Internet of things. Based on the analysis of the interrelated factors of distance learning status data, the different quadrants are divided according to the real-time of learning status data acquisition. On the basis of different collection schemes, the priority level of real-time collection of learning state data in distance education is identified. This paper combines the design of real-time collection algorithm of distance learning state data, and realizes real-time collection of distance learning state data. Experimental results show that the proposed method can improve the efficiency and accuracy of data acquisition in distance education.

The application of English translators is affected by speech recognition technology. Current speech recognition systems use Hidden Markov Models for recognition, which are susceptible to interference from noise and the magnitude of the recognition object, resulting in low accuracy and efficiency of the recognition system. Aiming at the above problems, research and design an English translator speech recognition system based on deep learning. Design the system hardware support software function with the combination of FPGA and STM32F4 microprocessor as the core. After preprocessing the speech sequence collected by the English translator and extracting the features, the DNN neural network trained by the restricted Boltzmann machine is used to recognize the speech sequence features to realize the speech recognition function. In the experiment, the DNN neural network has better recognition performance than the HMM model. The designed recognition system takes an average of 23.5 ms to recognize and has a higher recognition efficiency.

The lack of process evaluation indicators in teaching quality evaluation will affect the evaluation effect. In view of the problems of low accuracy and efficiency of evaluation results in traditional methods, an online and offline mixed teaching quality evaluation method of English based on three-dimensional teaching is proposed. Establish a three-dimensional English teaching model, which is divided into three stages: teaching preparation, teaching implementation and teaching extension, and analyze the characteristics of English online and offline mixed teaching. According to the characteristics of three-dimensional teaching, the process evaluation index is introduced, and the index system is designed. Calculate the internal weight of indicators, rank them in order according to the degree of importance, and establish a teaching quality evaluation model to test the teaching quality. The experimental results show that compared with the evaluation method based on grey correlation analysis, neural network and decision tree classification algorithm, this method can still maintain high accuracy when the amount of data is large, so it is effective and scientific.

Because there are too many data about the effect evaluation of sports online intelligent education, and they are not combined with the specific situation of the school, the traditional online intelligent education effect evaluation system has some problems, such as the imperfect effect evaluation model, the slow uploading speed of the system and so on. In order to solve this problem, this paper designs a sports online intelligent education effect evaluation system based on deep learning algorithm. Hardware part: use the streaming transmission protocol of HTTP, design the server architecture, and connect the circuit components; software part: combine the dual-teacher teaching mode, obtain the sports online intelligent education objectives, build the effect evaluation model using deep learning, diagnose the special technical progress of students, combine various learning resources with the professional teaching classification of our school, and design the automatic evaluation function of system software. Experimental results: The upload speed of the sports online intelligent education effect evaluation system can reach 268.49 KB/s, which proves that the sports online intelligent education effect evaluation system combined with the deep learning algorithm is uploaded faster.

In view of the poor evaluation effect of the current intelligent education model teaching effect of electromechanical equipment technology specialty, this paper puts forward the evaluation method of the intelligent education model teaching effect of electromechanical equipment technology specialty, constructs the teaching management system of electromechanical equipment technology specialty, optimizes the related technical relationship of electromechanical integration specialty, and constructs the related technical relationship evaluation index of electromechanical integration specialty. Finally, the experiment proves that the teaching effect evaluation method of intelligent education model for electromechanical equipment technology specialty has high effectiveness and practicability in the process of practical application, and fully meets the research requirements.

In order to improve the teaching effect and improve the accuracy of the evaluation method of the reform effect, this paper introduces particle swarm optimization algorithm to realize the effect analysis of the ideological and political teaching reform. First, define the evaluation data file, build a reasonable reform evaluation system, obtain the relevant characteristics of user interests, mine user preferences, design the reform evaluation extraction function, determine the evaluation content according to the system, and complete the reform effect evaluation using particle swarm optimization algorithm. The experimental results verify the accuracy and reliability of this method in the reform evaluation process, which is of great significance.

The traditional evaluation method of Marxist theory course has the problem of long evaluation time. Therefore, this paper proposes a evaluation method of Marxist Theory Course Based on in-depth learning. First of all, it analyzes the current situation of classroom teaching of Marxist theory course, designs the evaluation index of, determines the weight of evaluation index, extracts the characteristics of evaluation index by using deep learning algorithm, and constructs the evaluation system of Marxist Theory Course in combination with the principles of scientificity, systematization, independence and operability of index selection, According to the analytic hierarchy process, the weight of teaching quality evaluation index is calculated, and evaluation of Marxist theory course is realized. The experimental results show that this method can effectively shorten the time of evaluation and has better evaluation performance.

Entrepreneurship plays an important role in economic development and social progress. Innovation and entrepreneurship teaching for college students is an important part of cultivating innovative talents. However, the evaluation of the teaching effect of innovation and entrepreneurship is not comprehensive enough. Therefore, based on collaborative filtering algorithm, an evaluation method of the teaching effect of innovation and entrepreneurship is designed. Firstly, according to the teaching practice, the evaluation index of innovation and entrepreneurship teaching effect is selected to comprehensively determine the teaching effect. On this basis, the evaluation model of innovation and entrepreneurship teaching effect based on collaborative filtering algorithm is constructed. The experiment shows that the designed evaluation method of innovation and entrepreneurship teaching effect is accurate, with an average error of 0.03%, which has certain application value.

Traditional teaching quality evaluation mainly selects qualitative evaluation as the main method, and quantitative evaluation is the supplementary method. The selection of indicators is complicated, and the weight of evaluation indicators is usually set based on experience, which leads to low evaluation accuracy and efficiency of the evaluation system. Aiming at the above problems, a fuzzy AHP-based legal education and teaching quality evaluation system is designed. Design the hardware architecture in which the embedded microprocessor collects the evaluation data of professional education and teaching quality, and the FPGA processes the evaluation data. Use association rules to mine the correlation between factors in the evaluation information, and establish an evaluation index system for the teaching quality of law majors. The AHP method is used to determine the index weight, and the fuzzy theory is used to determine the index membership degree to realize the evaluation of teaching quality. The system test results show that the response time of the designed system is within 2.45 s, the average evaluation accuracy is 90.48%, and the user satisfaction is more than 80%. The evaluation response speed is faster, the efficiency is higher, and the user satisfaction is better.

The current pre-school education curriculum assessment and evaluation system is easily affected by other factors in the implementation process, resulting in poor evaluation results and affecting the evaluation efficiency. Therefore, this paper proposes a pre-school education curriculum assessment and evaluation system based on big data, optimizes the pre-school education curriculum objective system structure, constructs a pre-school education curriculum assessment and evaluation algorithm in combination with big data technology, constructs a standardized pre-school education curriculum management system, supervision and evaluation system, and verifies through experiments that the pre-school education curriculum assessment and evaluation system based on big data has high practical value and fully meets the research requirements.

Aiming at the problem that there are few preschool teachers with scientific and technological literacy and the training of preschool education professionals does not conform to the natural trend, in order to make the training of talents more convenient and more conducive to the formation of personalized education programs, the application of artificial intelligence in the training of preschool talents is studied. What the society needs now is preschool teachers with scientific and technological literacy. These preschool teachers can cultivate children’s awareness of innovation and technology. As future kindergarten teachers, they should be compound talents with “content + technology + education” and “pre-school education + artificial intelligence”. The training of pre-school education professionals with “pre-school education + artificial intelligence” should pay attention to the integration of artificial intelligence and training programs, and adhere to the educational vision of artificial intelligence education; The integration of artificial intelligence and personality teaching, in-depth excavation of the “embodiment” of the current situation of artificial intelligence education; Combine artificial intelligence with teaching evaluation to highlight the era value of artificial intelligence education; The integration of artificial intelligence and labor spirit will promote the spirit of Chinese model workers and craftsmen.

Aiming at the problem of inaccurate evaluation of traditional Japanese teaching ability, an online education effect evaluation algorithm of basic Japanese course based on data mining algorithm is proposed. Using data mining algorithm to collect the online education content of basic Japanese course; Establish the constraint parameter index, optimize the evaluation algorithm, improve the quality of teaching evaluation, and achieve the goal of Japanese teaching ability evaluation. Finally, experiments show that the online education effect evaluation of basic Japanese course based on data mining algorithm has good evaluation ability, and improves the accuracy and efficiency of teaching ability evaluation.

With the extensive development of network teaching, it has become one of the most important teaching methods at present. It is necessary to evaluate its teaching quality and give better play to its effect. This paper introduces analytic hierarchy process into this field and designs a new online and offline mixed teaching quality evaluation method of higher mathematics. First, the evaluation index is selected. Based on the judgment matrix constructed by AHP, the corresponding hierarchical weights are obtained by maximizing the eigenvalues of different matrices. The evaluation model is established. When the number of iterations reaches the maximum, the evaluation results of teaching quality are obtained. The experiment shows that the average evaluation accuracy of this method is 97.26%, and the effect is good.

In view of the poor application effect of current MOOC learning efficiency evaluation methods, this study proposed an intelligent evaluation method of MOOC learning efficiency based on Corleone model. Firstly, corleone model is used to select evaluation indexes of MOOC learning efficiency, and an intelligent evaluation system of teaching learning efficiency is constructed. Then optimize the teaching efficiency intelligent assessment steps. The experimental results show that the method has high practicability and accuracy in practical application, and the students can master the knowledge better after applying the method.