Analysis of Physical Education Curriculum Reform Strategies Based on Machine Learning

As a key link in the education system, physical education (PE) curriculum reform and innovation have also received extensive attention. This article aims to explore the reform strategy of PE curriculum based on machine learning (ML) to improve students’ sports skills and health level. To achieve this goal, we introduce collaborative filtering (CF) algorithm to make individualized recommendations for PE courses. By collecting and analyzing students’ historical data, sports preferences and physical fitness, this study constructs an effective recommendation model. The results show that the model can accurately identify students’ needs and recommend suitable PE courses for them, thus significantly improving students’ participation and satisfaction. Compared with the traditional PE curriculum model, the reform strategy proposed in this article is more intelligent and individualized, which is helpful to cultivate students’ innovative spirit and practical ability. To sum up, the reform strategy of PE curriculum based on ML has a wide application prospect in the field of education, which is expected to inject new vitality into future PE.