Machine Learning Model Construction and Practice for Personalized Training Programs in Physical Education and Sport Teaching

The implementation process of this paper to develop a personalized training program for physical education is mainly as follows: outlier processing, standardization, and correlation analysis of students’ physical education test scores. Then, the processed data were downscaled using principal component analysis and cluster analysis was performed. Finally, the BP neural network algorithm is used to predict the personalized exercise program, and the predicted program is adjusted with the help of NLP sentiment analysis. The practical analysis shows that the correlation coefficient between 50m running and standing long jump is −0.7483. The accuracy of the BP neural network model in predicting personalized exercise programs is 96%, and the adjusted and optimized personalized exercise program receives better feedback.