# Evaluation of sports training methods and technical characteristics based on multi-dimensional driving fuzzy intelligent computing

## TL;DR

This paper uses fuzzy intelligent computing to evaluate badminton training methods and analyze player techniques, showing improved performance over traditional approaches.

## Contribution

The novelty lies in applying multi-dimensional fuzzy intelligent computing to sports training and technical analysis of badminton players.

## Key findings

- The understanding degree of motion decomposition was 17.75% higher with the new method compared to traditional training.
- The new method showed better lasting effects and performance in mock exams compared to conventional training.
- The number of action repetitions was lower in traditional training methods.

## Abstract

To improve the competitive state of badminton athletes and summarize the technical characteristics of badminton players, this paper introduces multi-dimensional fuzzy removal intelligent computing. Taking 120 badminton students from a sports school as data samples, the sports images of athletes are collected, the images are enhanced using histogram equalization, and then the fuzzy clustering algorithm is used to analyze the characteristics of the pictures. The following results were obtained from the analysis of the understanding degree of motion decomposition, the analysis of the lasting effect, the study of the number of repetitions, and the analysis of the simulation results: The degree of understanding was 17.75% higher than that of traditional training methods; the effect was better than that of conventional training methods; the traditional training method had a small number of action repetitions; the performance of boys and girls in the temporary mock exam would be related to different training methods. Therefore, this paper had practical significance for this research, to help promote such academic and give reference. At the same time, most optimization problems needed to comprehensively consider many factors, so multi-objective optimization algorithms became a hot spot in academic research.