Three-dimensional (3D) point cloud data are widely used in intelligent driving, remote sensing, and virtual reality. This study presents a 3D point cloud classification algorithm that classifies large outdoor scenes effectively and accurately. First, the algorithm eliminates outliers from the original point cloud. Then, based on the off-the-shelf ground-filtering algorithm, it leverages difference of norms to filter ground points. Then, it uses the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm to segment non-ground point cloud. The nearest fusion strategy is used to solve the oversegmentation problem of the point cloud. Then, the proposed algorithm extracts global features that represent different objects from the point cloud, including vertical slice sampling and centroid distance histograms, as well as histogram of oriented gradient (HOG) features representing a two-dimensional projected image of the point cloud. Finally, a support vector machine (SVM) classifier is used to obtain the accurate 3D point cloud classification results. The experimental results reveal that the proposed algorithm can classify complex large outdoor scenes into accurate single objects with high accuracy and high recall rate. The proposed algorithm is more efficient compared with other algorithms.