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Mammography is the primary method for the early detection of breast cancer, but the results is largely limited by the radiologist's experience in clinical diagnosis. The study of automatic classification of mammography images based on convolutional neural network can provide advice for radiologists in clinical diagnosis, however the classification task of mammography images faced with many challenges due to the fuzzy edge and small difference between benign and malignant tumors. In order to improve the accuracy of mammography classification, an improved optimization algorithm based on Xception model was proposed, the residual connection module in the model is improved, and Squeeze-and-excitation(SE) attention mechanism is embedded to optimize the model. The optimized Xception model combined with transfer learning algorithm was used to feature extraction of mammography images, and the full-connection layer network was optimized for image classification. Experiments were conducted on the open data set CBIS-DDSM, and mammography images were automatically divided into benign and malignant. The experimental results showed that this method could effectively improve the classification effect of the model, and the accuracy and AUC reached 97.46% and 99.12%, respectively.