UAV lidar point cloud has many features, and the first matching takes a long time, so it is difficult to carry out the second matching. Therefore, the UAV lidar point cloud matching method based on two-dimensional normal distribution is studied. Collect the UAV lidar point cloud image, preprocess the image through the rotation translation method and Bilateral filter method, use the two-dimensional normal distribution algorithm and the dynamic time warping algorithm to complete the point cloud feature extraction, use the initial transformation matrix estimation algorithm to rough match the point cloud, and then use the near point iteration algorithm to fast and fine match the point cloud, and realize the UAV lidar point cloud fast matching through two matching. The experimental results show that the proposed method has good denoising effect on unmanned aerial vehicle LiDAR point cloud images, short point cloud matching time, and a matching deviation of only 0.04 m-0.15 m. The matching accuracy has met the relevant expectations.