In order to improve the accuracy and efficiency of dangerous target boundary crossing laser recognition, a research on dangerous target boundary crossing recognition based on laser image feature extraction is proposed. Using nonlinear direct transformation method to achieve scale invariant feature point matching in laser point cloud reflection image conversion; Design an anti sharpening dual mask image enhancement structure to optimize image enhancement processing; Perform Walsh transform and fusion on the reflection feature points of the point cloud, and use the BP neural network structure to identify dangerous targets that are out of bounds. The experimental results show that the proposed method enhances the detailed information of image features after application, does not have local exposure issues, has the lowest false detection rate and missed detection rate for dangerous targets exceeding the boundary, and has high recognition accuracy and efficiency.