Objective To develop an artificial intelligence (AI) model for analyzing pathological sections of lung tissue and providing real-time auxiliary diagnosis based on the deep learning algorithm. Methods Pathological sections of lung lesion tissues from 952 patients obtained by surgery or ultrasound/computed tomography guided biopsy in the Department of Thoracic Surgery and Respiration, Tongji Hospital and the Department of Ultrasound, Shanghai Pulmonary Hospital, Tongji University from Jul. 2019 to Feb. 2020 were retrospectively collected, including 254 (26.68%) cases of squamous cell carcinoma, 278 (29.20%) cases of adenocarcinoma, 47 (4.94%) cases of other malignant tumors, and 373 (39.18%) cases of benign lesions. According to the pathological subtypes, the slices were divided into training set (476 cases), validation set (286 cases) and test set (190 cases) using stratified random sampling (5:3:2). In each slice of the training set, 10 single field images (×400) were randomly captured for the training of YOLO (you only look once) v3 and Google Inception v3 networks, and then the module for segmenting benign and malignant regions and the module for classifying pathological subtypes were developed. Finally, the AI model with two parallel modules was constructed. In the validation set, single field images were captured in the same way to compare the diagnostic ability between the model and the pathologists. In the test set, two pathologists read the whole slices under the microscope for diagnosis, and one of them would get additional auxiliary diagnostic information from the AI model to compare their diagnostic ability. Results There were 2 860 single field images in the validation set, including 1 700 (59.44%) of malignant lesions and 1 160 (40.56%) of benign lesions. The sensitivity of benign and malignant differentiation of the model was better than that of pathologists (100%[1 700/1 700] vs 99.65%[1 694/1 700], χ²=4.167, P=0.031) and the accuracy of pathological subtype classification was similar to that of pathologists (95.52%[2 732/2 860] vs 94.30%[2 697/2 860], P>0.05). However, the overlap rate of segmented area with the gold standard ([92.72±12.75]% vs[95.42±6.99]%, t=7.628, P=0.001), specificity (97.67%[1 133/1 160] vs 99.31%[1 152/1 160], χ²=12.000, P=0.001) and accuracy (99.06%[2 833/2 860] vs 99.51%[2 846/2 860], χ²=4.364, P=0.037) of benign and malignant differentiation of the model were lower than those of pathologists. The test set consisted of 190 pathological sections, including 117 malignant lesions and 73 benign lesions. There were no significant differences between the pathologists who assisted by AI model and who did not in the accuracy rates of benign and malignant differentiation (100%[190/190] vs 99.47%[189/190], P>0.05) or classification of pathological subtypes (96.84%[184/190] vs 93.68%[178/190], P>0.05). However, it took significantly less time for the pathologist to diagnose with the AI model[(12.53±10.93] s vs[79.95±40.02] s, t=28.939, P<0.01). Conclusion The AI model based on deep learning algorithm can help pathologists quickly analyze lung tissue pathological sections stained by H-E staining, which can effectively improve the sensitivity and work efficiency without reducing the accuracy.