The early detection and precise pathological classification of gastric cancer can effectively improve the possibility of cure, posing higher demands on limited medical resources. In response to the various sources of classification for gastric cancer and the shortage of pathologists, this paper, for the first time, constructs a gastric tumor pathological classification recognition system using the ResNet-50-based DeepLab v3 semantic segmentation algorithm. This system assists pathologists in achieving rapid, efficient, and accurate collaborative diagnostic classification. For cases without malignant tumors, this paper also implements the binary classification recognition of low-grade intraepithelial neoplasia in the stomach. After training and testing on 1854 digitally annotated slices of gastric tissue from the Chinese PLA General Hospital, pixel-level annotated by experienced physicians, the system achieved a classification diagnosis accuracy of 61. 8% with a kappa value of 0. 496 for cancer zone identification. For low-grade intraepithelial neoplasia diagnosis, it attained a sensitivity of 100% , a specificity of 75. 8% , and an AUC of 0. 972. This paper presents the first implementation of gastric cancer classification diagnosis, capable of identifying cancerous areas and providing diagnostic references. Additionally, the system demonstrates high sensitivity and relatively accurate results for diagnosing low-grade intraepithelial neoplasia.