Spaceborne multibeam photon-counting LiDAR can achieve high repetition frequency detection, effectively improving the spatial resolution of LiDAR on-orbit measurements and meeting application requirements, such as surveying, mapping, and vegetation measurement. Aiming at the characteristics of photon-counting LiDAR point clouds, an adaptive denoising algorithm is proposed in this paper. First, the shape of the search area is optimized and the distribution characteristics of the neighborhood noise point density are analyzed. Then, identification parameters of the noise points are adaptively determined according to the statistical characteristics of the neighborhood noise point density. Experimental results of point cloud data obtained using the airborne prototype show that the measurement accuracy of the algorithm on roof ridge lines can reach 0.13--0.27 m. Experimental results of the multiple altimeter beam experimental LiDAR airborne experimental point cloud show that the recognition rate of the algorithm for typical scenes, such as ice sheet, sea surface, vegetation, and land, is better than 94%, and the accuracy rate is better than 90%. This shows that the algorithm has good adaptability and can be applied to adaptive denoising of large-scale photon-counting LiDAR point clouds.