With the development and application of airborne radar technology, it is faster and more convenient to obtain the spatial distribution of ground objects. To realize the terrain classification for airborne radar point cloud data, we first adopt the elevation histogram to remove the outliers of the original point cloud. Then, the voxelgrid filter is used to sample the point cloud, which can greatly reduce the number of points while maintaining the shape characteristics of the point cloud. Next, the K-means clustering method is improved. The K value and initial clustering center are determined through the height information of three-dimensional point cloud data, and the point cloud surface roughness is used for cluster analysis to realize the classification of different ground objects. The experimental results on the collected data and the open data show that the proposed method has high accuracy for point cloud data classification, with the overall classification accuracy reaching 88.17% and 90.47%, respectively, and the Kappa coefficient being 0.81 and 0.85, respectively.