Laser radar requires accurate calibration before operation, otherwise it can easily cause distortion in the captured images. To ensure the quality of shooting and address the issue of insufficient accuracy in traditional calibration methods, a target self calibration method for unmanned aerial vehicle (UAV) airborne LiDAR imaging is studied. Implement denoising processing for target point cloud images captured by airborne LiDAR. Construct a point cloud matching model using the KCRNet network in deep learning to achieve feature point cloud matching. Based on feature point pairs, construct a calibration parameter optimization model. Using genetic algorithm to solve the model, obtain the optimal calibration parameters, and complete the target self calibration of unmanned aerial vehicle aerial LiDAR images. The results show that after calibration, the gross error rate is relatively smaller, the highest value is only 2.6%, the average calibration accuracy is 99.2%, and the average calibration time is only 5.0s, which indicates that the calibration effect of the method is better and the image quality is higher.