A cloth simulation filtering algorithm based on an adaptive local filtering threshold was proposed to address large rejection errors in the ground and nonground point filtering results of airborne LiDAR point cloud data corresponding to suburban terrain environments using traditional cloth simulation filtering. First, the classic cloth simulation algorithm was used to extract the initial detected ground points and perform interpolation fitting to obtain a rough terrain surface. Then, combined with the adaptive filtering threshold calculation method based on local slope change rate, the filtering threshold of each point was automatically derived. This enabled determining the height difference between each point and the corresponding elevation of the fitting surface for efficient point cloud filtering. Experimental results show that the proposed algorithm can effectively improve the accuracy of ground point extraction compared with traditional cloth simulation filtering and accurately extract ground point clouds in large-scale complex environments such as suburban areas.