Clustering filtering is a practical method according to the characteristic attributes of the lidar point cloud. However, because of the large data size of the point cloud, direct clustering using three-dimensional point coordinates is time-consuming, produces large filtering error results, and existing filtering algorithms do not perform well in discontinuous terrain. In this paper, we proposed a new point cloud filtering algorithm based on density clustering to solve the direct clustering problem of large-scale point clouds and preserve the overall fluctuation of discontinuous terrain. First, based on the spatial density of lidar point cloud, the characteristic attributes of both ground object and terrain point clouds cluster according to the elevation value density of point cloud, and then screen the plane point cloud, to reduce the number of samples of data. Finally, the original point cloud is divided into noise, nonground, and ground point clouds using density-based spatial clustering of applications with noise algorithm. The experiment is conducted with data samples provided by the international society for photogrammetry and remote sensing. Furthermore, we compared the proposed algorithm with eight other classical filtering algorithms. The quantitative and qualitative results show that the proposed algorithm has good applicability in urban and rural areas, with small filtering error in discontinuous terrain and good adaptability in the mixed area of artificial buildings and vegetation. The proposed algorithm is feasible and can be used in different terrain.