Breast cancer is one of the diseases that pose a serious threat to women′s health. Early diagnosis is crucial for the cure of breast cancer, and computer-aided breast cancer classification and diagnosis has been widely used. Although the mask autoencoders breast cancer classification method can improve the model performance under the premise of the lack of labeled data in breast cancer pathology images, the existing mask autoencoders breast cancer pathology image classification method does not adequately extract and fuse the feature information between breast cancer pathology images with different magnifications. To solve this problem, a multi-magnification mask autoencoders breast cancer pathology image classification method is proposed, which combines the advantages of magnification independence and magnification specificity on the basis of mask autoencoders. First, a uniform noise masked module is designed to avoid the loss of important features in breast cancer pathology images. Then, blocks of breast cancer pathology images with different magnifications are combined together and fed into an encoder incorporating cross convolution mapping to extract and fuse features from images with different magnifications. Finally, a residual cross attention mechanism is incorporated into the decoder to enhance the fusion of cell density and alignment order under low magnification images and cell texture features under high magnification images. Experiments on the BreakHis public dataset show that the proposed method improves at least about 2% in Top-1 Accuracy, Precision, Recall, and F1-Score compared to existing classification methods. The results demonstrate that the proposed method exhibits good performance in accurately classifying benign and malignant breast cancer pathology images.