Objective To construct an artificial intelligence (AI) diagnostic model for tumors based on digital pathology sections and validating its diagnostic efficacy. Methods Patients with tumors (lung cancer, stomach cancer, liver cancer, colorectal cancer, breast cancer, prostate cancer) at Huadong Hospital Affiliated to Fudan University and Shanghai Fifth People's Hospital Affiliated to Fudan University were selected from January 2022 to January 2024, with a total of 100 cases, and a total of 4,000 pathological tissue sections were collected, and the patients were divided into a training group and a validation group according to the ratio of 8∶2, with 80 cases and 20 cases, respectively. The dataset images were input into DeepLabV3+ semantic segmentation model, and the pathological features of the digital images were extracted and input into ResNet50 classification model for training, and two kinds of datasets were input into the training group to construct the tumor AI diagnostic model respectively, model 1 inputs the dataset with only pathological tissues (positive annotation), and model 2 inputs the dataset with pathological tissues and normal tissues (positive and negative annotations). The validation group was used to verify the diagnostic efficacy of the above two models. Comparison of HE staining, ROC curve analysis and diagnostic efficacy analysis of the two models for tumor diagnosis. Results HE staining showed that compared with model 1, model 2 could display the pathological tissue extent and boundary of the tumor more clearly, and better distinguish pathological tissue from normal tissue; the results of ROC curve showed that both model 1 and model 2 could diagnose the tumor efficiently (P＜0.05), and the AUC and Jordon's index of diagnosing the tumor in model 2 were larger than that of model 1, with obvious differences (P＜0.05); the Sensitivity, specificity, accuracy value and positive and negative predictive values were greater than model 1, with significant differences (P＜0.05).Conclusion Tumor AI diagnostic models constructed based on pathology digital sections have excellent diagnostic effects on tumors, in which positive and negative labeling models have higher diagnostic efficacy for tumors, which is worth clinical attempts and practice