Objective To establish an artificial intelligence (AI) model in the pathological diagnosis of skin tumors and to evaluate its diagnostic efficacy. Methods Two common skin tumors, Bowen's disease and seborrheic keratosis (SK), were selected as the target diseases. Training set and validation set were provided for AI by manually labeling the lesion areas in histopathological hematoxylin-eosin (H-E) stained sections. The two-stage diagnostic framework (patch diagnosis and slide diagnosis) based on deep learning in AI was used to make a comprehensive judgment, so as to establish the diagnostic model of the corresponding diseases. Histopathological H-E sections without labeling lesion areas were selected to provide test set for AI to verify the accuracy of the diagnostic model. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic and differential diagnostic efficacy. Results In the first stage of patch diagnosis, the Efficientnet_b6 model had a better effect for patch feature classification, and the sensitivity of the training set and validation set reached 94.67% (6 680/7 056) and 95.79% (751/784), respectively. In the second stage of slide diagnosis, the semi-structured data model (SSDM) was more effective in patch feature aggregation, and the specificity of the training set and the validation set was 95.00% (6 703/7 056) and 95.28% (747/784), while the specificity of the training set and the validation set of financial service data model (FSDM) was 91.16% (6 432/7 056) and 82.78% (649/784). When the two-stage diagnostic model was applied to the test set, the accuracy of Bowen's disease and SK was 92.65% (63/68) and 99.21% (126/127), respectively. ROC curves of the two-stage diagnostic model for Bowen's disease and SK were plotted, with the AUC values being 0.978 26 and 0.986 98, respectively; and the overall ROC curves were plotted using micro- and macro-average, with the AUC values being 0.989 89 and 0.983 54, respectively. Conclusion The two-stage AI diagnostic model proposed in this study has a higher diagnostic and differential diagnostic efficacy in the histopathological H-E sections of Bowen's disease and SK.