Herein, to improve the automation and accuracy of the airborne LiDAR point cloud data classification algorithm, a classification algorithm for point clouds based on improved progressive triangulated irregular network densification (IPTD) and a double-layer support vector machine (SVM) was proposed; the classification effect of the algorithm on urban point cloud data was tested as follows. The IPTD filter method was used to extract ground points, and ground points were normalized based on ground points. Then, the effectiveness of point cloud features was evaluated to select eigenvectors, and nearest-neighbor SVM (NN-SVM) was used to classify the ground feature points, realizing the multiple classification of the urban point cloud data. Furthermore, the classification algorithm was verified using point cloud data from urban regions, and the classification effect was evaluated by analyzing the classification accuracy. The experimental results show that this algorithm can effectively improve the classification accuracy and classify point cloud data in urban areas.