Objective To establish an artificial intelligence (AI) algorithm model for the diagnosis of hilar cholangiocarcinoma (HC) and to evaluate its ability to recognize tumor cells and perineural invasion (PNI). Methods AI algorithm was used for deep learning on 825 HC and 175 non-cancerous tissue images (600 for training set, 300 for test set, and 100 for comparison set). The neural network combined with GoogLeNet and DenseNet with different parameters was used for feature extraction and deep learning of HC cells and PNI. The AI algorithm model was compared among 3 pathologists (1 associate chief physician, 1 attending physician and 1 resident) in judging the presence or absence of tumor and the proportion of tumor cells. Results The AI algorithm based on deep learning could accurately identify tumor cells and PNI in HC tissue sample images. The AI algorithm was as good as experienced associate chief physicians in the department of pathology in diagnosing tumors, and was better in estimating the proportion of tumor cells. Conclusion AI algorithm model is helpful in identifying HC cells and PNI.