Aiming at the problem of low accuracy of road crack identification, a road crack identification method based on visual attention mechanism is proposed. Firstly, the source images are manually classified into non-crack, longitudinal crack, transverse crack, and grid crack. Then the corresponding texture information is obtained by Gabor filtering of the crack image, and the obtained texture image is segmented by K-means clustering algorithm. Then the attention-mechanism-based model SRM-Resnet (Style-based Recalibration Module Of Resnet) is used for classification and recognition. Finally, the proposed method is compared with several common attention-based models on four types of images. The results show that the road crack identification based on the improved visual attention mechanism proposed in this paper has greatly improved the accuracy of classification ability, and can effectively identify road cracks. The final identification accuracy can reach 0.966.