Wheels are one of the key components of the running part of the train.Defects in its tread will directly affect the safety of train operation.In order to accurately identify different types of wheel tread defects during inspection,a texture feature extraction method based on gray-gradient co-occurrence matrix is proposed.After analyzing the gray and gradient features of the tread image,image texture feature vector is extracted according to the gray-gradient co-occurrence matrix.Then combined with the K-means clustering optimization algorithm to cluster the characteristics of tread defects,thereby classifying the types of tread defects,and displaying the classification results with visual data.The experimental results show that the accuracy of classifying and identifying different types of wheel tread defects is over 96% by using the above-mentioned algorithm.