In order to improve the quality and output of industrial strip steels and address the problems of traditional manual identification such as identification difficulty, low efficiency and lack of objectivity, we propose a method for identifying strip steel surface defects based on the soft attention mechanism and improve the traditional deep residual network ResNet model. Moreover, we use the pseudo-color image enhancement technique to process images and obtain new training sets. The experimental results show that compared with the traditional models, the improved models of A-ResNet50 and A-ResNet101 can both accurately identify different types of strip steel surface defect images under different signal-to-noise ratios. The accuracies on the test set are 98.61% and 98.05%, and the unit inference time is 0.078 s and 0.130 s, respectively. Thus the feasibility and reliability of these two models in the identification of surface defects on strip steels are confirmed. The proposed method possesses a high identification accuracy, which can be used to realize the intelligent identification of surface defects on strip steels and meet the demands of industrial identification.