Prostate cancer ranks as the second most frequently diagnosed neoplasia and the fifth leading cause of mortality in male patients with cancer. It is of great clinical significance to design an image-assisted diagnosis system for prostate pathological section. In the case of only image-level annotation data set, convolutional neural network (CNN) is used to classify only images, but no cancerous regions are given. In view of this situation, the CNN model with efficientnet-B0 architecture was used as the basic classification model, the image was divided into patches and the categories of each patch were obtained, and the cancerous regions were obtained by clustering algorithm. Pathological images were uploaded on the Web front end, and auxiliary diagnosis results were viewed after the processing was completed. Experimental results show that the precision of the system is 76.61%, and the recall rate is 72.52%, which can effectively obtain the general area and obtain satisfactory auxiliary diagnosis effect.