Intelligent Generation System for Personalized Physical Training Programs and the Effect of Practical Application

Abstract

With the comprehensive construction of quality education, physical training is an important way to cultivate students’ physical quality, and its related construction is gradually receiving comprehensive attention and support. This paper discusses the intelligent generation scheme of sports training plans, which aims to meet the individual needs of students through algorithms while developing the best training plan for them. This paper first introduces the overall architecture of the sports training program generation system. Secondly, the association rule method is utilized to mine the sports training data, and after elaborating the concept of association rules, the FP-growth algorithm is proposed to carry out the data mining work with the FP tree at the very beginning as the core. Then, a decision tree model based on the ID3 algorithm is constructed to correctly classify the training program based on the attributes selected at each level of nodes in order to obtain the attributes with maximum information gain. An empirical analysis of students from all grades in a school showed that there is a correlation between the various sports training programs of male and female students. After using the sports training program generation system designed on this basis, the boys’ 50-meter run performance (P<0.01), boys’ standing long jump performance (P<0.05), and girls’ 50-meter run performance (P<0.05) were significantly improved.