Artificial Intelligence-Based Soccer Sports Training Function Extraction: Application of Improved Genetic Algorithm to Soccer Training Path Planning

Abstract

Artificial intelligence has given a new dimension to the sport and mentality of soccer by tracking and planning the path of soccer and analyzing the learning process. Along with the rapid development of artificial intelligence technology, it is also used in sports events. Soccer, the world’s number one ball game, has always received worldwide attention. Although soccer is full of realism, it does not mean that its behavior cannot be predicted. By taking advantage of the role of artificial intelligence, model data from training is used to perform a deeper analysis of the correlated role of a soccer team’s players in the game, which is very helpful to improve the team’s training efficiency and tactics. As an emerging intelligent algorithm, the improved genetic algorithm can seek the global optimal solution based on the results of the algorithm execution and apply it to the planning of soccer training paths, which has a strong application value for improving the team’s training level and helping the team to formulate reasonable and effective offensive and defensive strategies. In this paper, we use artificial intelligence as the research background to improve the genetic algorithm by extracting the soccer training function and apply the improved algorithm to the soccer training path planning to get the global optimal solution, so as to help the players find the reasonable and effective optimal path of passing or shooting and help the team to build the tactical planning and winning strategy of offense and defense to meet the earnest expectation of soccer fans.