Designing an Artificial Intelligence-based sport management system using big data

These days, improving students’ learning and work efficiency needs a comprehensive approach to their physical and mental well-being, which emphasizes the critical role of teachers and institutions. To optimize the responsibilities of physical teachers and coaches, efficient administration of physical education, including sports activities, and a strong quality assurance system are required. Based on the above, this paper proposes an Artificial Intelligence (AI)-based sports management system to enhance work efficiency and ease of physical education teachers and coaches. It aims to make an in-depth study on the construction of sports performance management and physical analysis system based on data mining; later, it includes the construction of sports management systems. This paper operates in such a way that it first discusses the sports performance management and physical analysis system's construction. Second, it analyses the overall framework structure of the system by presenting a detailed description of the sports test management and test items management. In addition, it analyses sports performance management and sports performance, system management and physical performance and other functional modules contained in the system along with the database design in the system based on C4.5 that is then used as the Decision Tree (DT) Classifier for data mining. Third, it uses sports performance management of DT, and the physical analysis algorithm uses the data information gained to select the split attributes. Fourth, it divides the sports performance training data set into multiple sub-datasets to construct the performance management and physical analysis system based on data mining. Finally, the suggested system is compared with existing approaches and obtains an accuracy of 97%, which is 27% high than the selected approaches. Simulation experiments show that the proposed method has high accuracy in data mining related to sports performance and can effectively improve the efficiency and quality of physical education teachers. The proposed system will be an addition to intelligent task management with high feasibility and accuracy.