Research on Cluster Analysis and Pattern Recognition of Students’ Sports Performance Data during Physical Education Instruction

In this paper, a K⁃Means clustering algorithm based on improved differential evolution (AGDE⁃KM) is proposed to design the adaptive operation operator, design the multi-variation strategy and introduce the weight coefficients in the variation stage to regulate the searching ability of the algorithm and accelerate its convergence speed. The Gaussian perturbation crossover operation based on the best individual of the current population is introduced, and the optimal solution output from the improved differential evolution algorithm is used as the clustering center to realize the cluster analysis of students’ sports performance data. Afterwards, the hierarchical recognition algorithm and support vector machine are used to recognize students’ sports patterns, and the wavelet transform algorithm is used to extract and select the students’ sports feature quantities, so as to improve the accuracy of students’ sports pattern recognition in sports teaching. In the process of physical education teaching, AGDE ⁃ KM algorithm is more pertinent to the clustering effect of students’ sports performance, and its explanatory degrees of Calinski-harabasz metrics, profile coefficients, and Dunn metrics are 860.0276, 0.3928, and 0.0486, which are 19.0382, 0.0435, and 0.0099. In addition, the AGDE⁃KM algorithm achieves 95.7625%, 99.75%, and 99.85% of the mean value of step recognition accuracy for different testers in the 50m, 800m, and 1000m events, respectively, which is a good recognition effect.