The vehicle safety and stability during the driving process is directly influenced by the tire wear, and the vehicle safety can be improved by detecting the degree of tire wear to find and process the abnormal state of the tire timely. The tire wear degree can be detected with the sensor installed in the tire or the tire image directly. However, the sensor installed method has high cost and cumbersome installation process, and the image-based detection method requires more samples and the detection accuracy is not high. Therefore, a tire wear detection method based on fused texture features is proposed in this paper. The training set was constructed with 25 tire images of 5 different wear degrees, and each image was uniformly cropped into 12 sub-images, and the gray level co-occurrence matrix and local binary patterns features were extracted by median filtering, and the fusion features were obtained by principal component analysis and stitching fusion method. Then, the classifier was trained by sparrow search algorithm and the random forest method with the fusion features. Finally, the algorithm was tested with 225 acquired images of different degrees of tire wear. The results show that the average detection accuracy reaches 97.33%, which is significantly higher than that of a single feature and other classification methods, so, the proposed method can be used to detect the tire wear quickly and accurately.