Analysis of the teaching quality on deep learning-based innovative ideological political education platform

Education ideology refers to believers, traditions, cultures and principles that govern education in economics, politics, morals, faith, knowledge and reality, aesthetics and artistic affairs. Different types of expenditure, protection against political risks, changes in government policies and trade ties are considered important factor in ideological and political education. In the paper, Deep Learning-Based Innovative Ideological Political Education Platform has been proposed to improve limitations on the functioning or restraint of multinational firms' access to teaching quality funding and trading. Information supervision quality analysis is introduced to reduce social threat perception by proper strategic assessment and implementation. Realization ideological education technique is implemented corporations use economic ties to address domestic problems. Based on experiment results, the educational activities and teaching became more effective, an educational intervention scheme is explained, and the plasticity of the brain optimized and ideological and political education performance is increased. The evaluation results of deep learning Radial Basis Function Neural Network in Innovative Ideological Political Education platform has been proposed to improve the teaching quality are calculating the mean percentage error ratio is 88.87%, reducing the political risk in teaching ratio is 83.86%, the evaluation students ratio is 76.80%, the predicting teaching error ratio is 79.13%, and the overall performance of the teaching quality is 86.55%.