Expert System in the Construction of Personalized Football Training Program Model

This paper focuses on building a personalized football training program formulation model based on expert system, aiming to solve the problem of personalized training needs of football players. After in-depth research on the relevant theories of football training, an algorithm based on improved particle swarm optimization and deep belief network (IPSO - DBN) is innovatively proposed. This algorithm effectively overcomes many shortcomings of traditional algorithms in the formulation of training programs, and can accurately generate personalized training programs based on the individual characteristics of athletes. The experiment selected 50 football players and randomly divided them into an experimental group and a control group, with 25 people in each group. The experimental group adopted a personalized training program generated based on this model, and the control group adopted a traditional training program. After 3 months of training, the athletes' physical fitness, technology and other indicators were evaluated. The results showed that the average physical fitness improvement rate of the experimental group athletes reached 18%, while that of the control group was only 10%; in terms of technical improvement scores, the experimental group increased by an average of 8 points, and the control group increased by an average of 4 points. The experimental results strongly show that the model constructed in this paper can significantly improve the effect of football training and provide a more scientific and personalized method for football training. In the future, the model will be further optimized to improve its applicability in different scenarios.