Abstract: Microstructure analysis is an important analysis method in the research and development process of iron and steel materials. At present, it is mainly judged manually by experts with rich experience, which is time-consuming and easily affected by subjective consciousness. Therefore, an intelligent analysis method for microstructure based on the residual neural network structure is studied. By improving the residual network model, an improved residual network model based on transfer learning and a deep residual shrinkage network model based on the attention mechanism are proposed. Two different convolutional neural network models are used for verification on the microstructure test set of 20 steel materials. The experimental results show that the accuracies of the two models reach 95.36% and 95.79% respectively, with strong generalization ability, and the shortest average prediction time is only 1.66 s per image. The two models have certain advantages in the classification of microstructure features of iron and steel materials, realizing the automation and intelligence of microstructure type classification.