Intelligent unsupervised learning method of physical education image resources based on genetic algorithm

In order to improve the effect of intelligent processing of physical education resources, this paper combines genetic algorithms and unsupervised learning methods to study the processing of physical education videos and images, which approximately maintains the neighbor structure relationship of the original sample data and reduces the loss of sample local information. Moreover, this paper constructs a graph model structure between the comprehensive compressed data and the input data. In order to reduce the complexity of the solution method of the graph model and reduce the impact of the method of decomposing the spectrogram into the characteristic function on the hash algorithm, this paper designs an intelligent physical education resource processing method based on genetic algorithms and unsupervised learning methods and constructs system functional modules. Finally, this paper designs experiments to verify the performance of the method proposed in this paper. Through comparative analysis of experiments, it can be known that the method proposed in this paper has good results.