In response to the problem that the elevation values between adjacent pixels in the digital elevation model are prone to discontinuity with the surrounding terrain features, which affects the data filtering effect, this study proposes an airborne LiDAR point cloud data filtering method based on elevation jump. The surrounding terrain features are an important basis for filtering airborne LiDAR point cloud data, and the elevation data between adjacent pixels is processed to achieve data filtering. This method first deeply analyzes the data characteristics of airborne LiDAR point clouds and the unique attributes of various ground objects, accurately assigns corresponding binary signals to each point cloud, and introduces them into the BP neural network to achieve fine classification of airborne LiDAR point clouds. Then, based on the results of point cloud classification, combined with the topological adjacency relationship of irregular triangulation networks, dynamic thresholds are set considering elevation factors and spatial angles to achieve filtering processing of ground feature data within any elevation range. Finally, virtual grid technology was introduced to process all point cloud data, combined with local adaptive threshold methods, to filter the remaining non ground point data. Experimental studies have shown that the proposed method has strong adaptability and can achieve good filtering effects on airborne LiDAR point cloud data, with greater application value.