Abstract

Due to the rapid development of modern technology, nowadays there is a need to process and analyze all types of student data and thus optimize physical education management methods. In this paper, the maximum information entropy model is proposed based on the information entropy model of fuzzy rough sets, and a new algorithm FS-mMc is designed based on this model. this algorithm is capable of processing mixed information such as discrete, continuous and fuzzy data simultaneously in order to calculate the entropy weight of each indicator and thus obtain the important factors that make students burnout. A comparison of the classification accuracy of this algorithm with CFS, Relief, and InfoGain algorithms under different conditions yielded that the accuracy of this algorithm improved by at least 16.42% over the other algorithms with at least half the number of features selected. In the analysis of the factors influencing students' burnout in physical education, the mean difference between male and female students in terms of gender was found to be 0.25, between freshmen and juniors in terms of grade was 0.36, between sophomores and seniors in terms of grade was 0.47, and between urban and rural areas in terms of difference was 0.26. Therefore, the algorithm proposed in this paper has a higher accuracy in data processing and analyzes the main factors influencing students' burnout in terms of factors, thus improving the physical education management ability.