Deep learning has achieved good detection results in the detection that surface defect detection of Sheet Metal Strip.However,as the number of network layers increases,the train ing data of strip surface defect data set for smaller samples is prone to overfitting.For this reason,combining the residual network and transfer learning,an image classification algorithm combining multi-level defect featur es is proposed.The algorithm uses the residual network module to extract the metal surface defect features la yer by layer,obtains a feature map of rich location information and semantic information defect features,and t hen uses the classification network to obtain the final classification result based on the fusion feature ma p,at the same time,migration learning is performed on the feature extraction network to increase the generali zation ability of the network and optimize the classification accuracy.Using the steel strip surface defect detec tion data set to evaluate the performance of the algorithm in this paper,the experimental results show that t he proposed algorithm has a better classification effect than other defect classification algorithms,and th e classification accuracy rate can reach 99.07%.Simultaneously,the algorithm has good noise resistance and genera lization,and has good application value in the intelligent detection of surface defects of metal plate s and strips.