Since the volume of point cloud data captured by a three-dimensional laser scanner is large and leads to redundancy, occupying a lot of computer space and time cost in the later data processing. Thus, the point cloud data processing must be simplified. A hierarchical point cloud simplification algorithm is proposed on the premise of retaining the key geometric features for aiming at the scattered point cloud data model. First, the point cloud model's cuboid bounding box was constructed and divided into multiple small cube grids, so that each point was contained in the grid. Further, the weight of each point in each grid was estimated, and whether the point was preserved or not was determined by comparing the weight and weight threshold, to eliminate the noise points and achieve the point cloud's initial simplification. Finally, the simplification algorithm based on curvature classification was employed to achieve the point cloud's fine simplification. Through the simplification experiments of the common and cultural relic point cloud data model, the results demonstrate that, when compared with the random sampling, uniform grid, and normal vector angle approach, the algorithm has better geometric feature preservation performance, and can achieve better point cloud simplification effect that is an effective point cloud simplification algorithm.