摘要Breast cancer is a serious and high morbidity disease in women,and it is the main cause of cancer death in China.However,getting tested and diagnosed early can reduce the risk of cancer.At present,there are clinical examinations,imaging screening and biopsies,among which histopathological examination is the gold standard.However,the process is complicated and time-consuming,and misdiagnosis may exist.This paper puts forward a classification framework based on deep learning,introducing multi-attention mechanism,selecting kernel convolution instead of ordinary convolution,and using different weights and combinations to pay attention to the accuracy index and growth rate of the model.In addition,we also compared the learning rate regulators.Error function can fine-tune the learning rate to achieve good performance,using label softening to reduce the loss error caused by model error recognition in the label,and assigning different category weights in the loss function to balance the positive and negative samples.We used the BreakHis data set to automatically classify histological images into benign and malignant,four categories and eight subtypes.Experimental results showed that the accuracy of binary classifications ranged from 98.23％to 99.50％,and that of multiple classifications ranged from 97.89％to 98.11％.