Personalized Optimization of Sports Training Plans Based on Big Data and Intelligent Computing

Abstract

In order to effectively improve the athletic performance of athletes and make their training more systematic, scientific, and standardized, the author proposes a personalized optimization study of sports training plans based on big data and intelligent computing. Based on the research on big data and intelligent computing, the author designs a human-computer interaction system using association rule mining algorithms, data fusion processing in sports training decision support systems, and improved Apriori algorithm frequent association rules. A sports training plan leveraging big data and intelligent computing was developed through experiments. The findings reveal that the author’s enhanced Apriori algorithm exhibits a reduced reaction time when subjected to a minimum support threshold. Specifically, with a minimum support of 3.5, the execution time is under one second. This demonstrates that the improved Apriori algorithm can efficiently facilitate decision support for sports training regimes, offering valuable insights for athletes’ physical conditioning. The research on personalized optimization of sports training programs, utilizing big data and intelligent computing, enables athletes to access real-time sports data, thereby enhancing their performance.