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In order to solve the demand for damage assessment of a large number of reinforced concrete (RC) frame communication buildings after earthquakes, this paper studied the damage assessment methods from the component level to the overall structure based on convolutional neural networks (CNN). Firstly, a large number of damage survey pictures of RC frame structures after earthquakes such as the Wenchuan earthquake, Ludian earthquake, and Lushan earthquake were screened and processed, and a damage assessment dataset of RC frame beams and columns was established. Secondly, a damage assessment method for RC frames based on CNN was established through the study of 3 key issues: The task of detecting and recognizing the components of beams and columns from the photos of structural damage was completed by training and establishing the YOLOv5 network model; the detection performance of the YOLOv5 network model was improved and optimized; 3 network models (ResNet50, MobileNet V2, and AlexNet model) were selected and compared for the accuracy of damage level assessment of beams and columns. Finally, a damage assessment model of beams and columns based on ResNet50 was established. The method of determining the damage level from the component level to the overall structure was given, and the availability of the method in the paper was verified by damage assessment of an actual damaged frame, and the results show that the method in the paper has high consistency with the damage assessment conclusion of experts, and the optimized CNN model has good accuracy and stability, and has good applicability to the damage assessment of post-earthquake RC frame structures. [ABSTRACT FROM AUTHOR]