A study on the optimization of track and field training strategies under the integration of sports science and information technology

Abstract

With the development of sports science and information technology, how to use mining technology to analyze the correlation between various track and field training programs plays an important role in the improvement of track and field training. A decision support system for track and field training is constructed using the Apriori algorithm and decision tree ID3 algorithm. By mining and analyzing different sports data of track and field athletes, the system blends scientific training theory and advanced training methods to create a set of reasonable track and field training programs for athletes. Through empirical research, after the optimization of the track and field training decision support system, 72.2% of the athletes whose physical fitness test 30-meter run scores were between 80 and 100 had bar pull-up scores between 0 and 55. The average performance of students’ 3000-meter run improved by 81.35s.