With the development of a smart grid, the automatic location of power equipment is becoming a trend. In this study, a method for automatic location identification and diagnosis of external power insulation equipment based on YOLOv3 is proposed. This deep learning algorithm is used to extract the characteristics of image data under the visible light channel of the insulator. It learns and trains the collected data to realise the rapid location identification and frame selection of the external insulation equipment and extract discharge characteristics of the target box under the ultraviolet channel. According to the number of photons and the spot area information, the operating status of the equipment is determined. The results show that the YOLOv3 algorithm with a training rate of 0.005 achieved a fast convergence of the location recognition model. The average recognition accuracy was 88.7% and the average detection time was 0.0182 s. The combination of visible light path insulator target recognition and ultraviolet light path diagnosis can realise a lean and intelligent diagnosis of power equipment. This method had good real-time performance, accuracy, and robustness to the background. It provides a new concept for intelligent diagnosis and location analysis of power equipment.