Aiming at the characteristics of strong randomness and wide distribution of cancer cells in histopathological images,and it is difficult for common convolutional neural network (CNN) to directly obtain long-range dependencies,this thesis adopts ResNet based on transfer learning and optimized Non-local Net.The combined non-local convolutional residual model fuses location and channel features to extract global information in images for selecting valuable regions for classification.In this thesis,the BreakHis dataset is used for experiments.In view of the extremely unbalanced distribution of benign and malignant samples in the dataset,the Random-SMOTE algorithm is used to balance benign and malignant samples to strengthen the model's ability to learn minority class features.On the datasets that do not distinguish multiples,the PR,RE,SP and ACC of the proposed method reach 93.28%,98.71%,98.67% and 98.70%,respectively;on the datasets with known multiples,the above indicators are also higher.Compared with the commonly used algorithms in breast cancer histopathological classification,the method proposed in this thesis has better performance.