Damage to wind turbine blades can easily lead to wind power equipment failures, posing a threat to personnel safety. The existing methods for fan-blade damage detection require stopping the fan blades, which is time-consuming and costly.A photoelectric detection and recognition method for blade damage based on pulse laser synchronization is proposed to address this issue.First, a 535 nm sub-nanosecond high-frequency laser is designed to irradiate the observation area of the fan blades. When the blade rotates to the laser path, an echo signal is generated, which is detected and converted into an electrical pulse signal by the silicon photodiode receiving module. This further triggers the large format camera Phase One to take photos within the field of view. After obtaining the blade image, deep learning algorithms are used to segment and extract the image blades through a priori data samples, and based on the YOLOv5 algorithm, wind turbine blade damage detection and recognition are implemented, outputting the damage category and locating the damage location. Experimental results show that the proposed method effectively improves the fan maintenance efficiency and offers a higher blade-damage-detection accuracy than conventional methods. The results of this study can provide a reference for the health monitoring of fan blades.