Objective To develop an artificial intelligence (AI) system that could automatically identify the glomerulus on the renal pathological section images based on cascade region-convolutional neural network (cascade R-CNN) algorithm, and to help pathologists to calculate the number of glomerulus and identify glomerulus. Methods Renal pathological sections from patients undergoing renal biopsy in the Second Hospital of Shanxi Medical University and People's Hospital Affiliated to Shanxi Medical University from 2017 to 2019 were collected. Totally, 1 180 periodic acid-silver metheramine (PASM) stained section images of similar quality were included after eliminating the blurred and poor quality ones. The digital scanned images were obtained by high-resolution whole slide image (WSI), and the image data were transmitted and stored to the cloud through the remote pathology system. A training set (940 images) and a test set (240 images) were created by cascade R-CNN. The training set was used to train AI to identify the glomerulus, and the test set was used to test and evaluate the precision and recall rate of cascade R-CNN algorithm to identify the glomerulus. The pathological sections of the test set were read by 3 pathologists who had worked for at least 3 years, and the precision and time for the pathologists to identify the glomerulus were calculated. Results The identification time of each glomerular region imnage was (0.20±0.02) s using the deep learning model trained by cascade R-CNN network. The precision and recall rate were 93.90% and 98.00%, respectively, and the F1 value was 95.91%. The time for the 3 pathologists to identify each glomerular region image was (3.57±0.05), (4.57±0.07), and (3.98±0.02) s, and the precision was 88.08%, 89.69%, and 89.98%, respectively, with no significant difference (all P>0.05). The precision of the cascade R-CNN algorithm was significantly higher than those of the 3 pathologists (89.25%) (t=-5.607, P=0.009). Conclusion cascade R-CNN algorithm can quickly and effectively identify glomerulus through high-resolution WSI, and it can help pathologists improving the diagnosis efficiency of renal diseases.