Based on the YOLOv4 target detection method, in this study, we proposed an intelligent insulator burst fault recognition model. Considering images of normal and burst insulators in a power supply bureau within one year as samples, the proposed model was trained to obtain its weight. The proposed model was further used to identify insulators and their bursting faults. Experimental results showed that the proposed model had an average precision of insulator positioning of 92.6%, an average precision of insulator burst fault location of 91.78%, and the model's resolution was 46 frame/s. Compared with Faster R-CNN and SSD models, the constructed insulator burst fault identification model can accurately and quickly identify insulators and their burst faults.