Airborne LiDAR point cloud features are abundant, but their density is uneven. Efficient classification for the airborne LiDAR point cloud is a key task in remote sensing and photogrammetry. Because the density of the point cloud is not uniform, a density-dependent point cloud convolution operator, PointConv, was introduced to perform density weighting on the basis of traditional three-dimensional (3D) convolution. At the same time, the attention mechanism module was proposed to correct the importance of extracted local information and improve the ability of the network for identifying different point cloud instances. The effectiveness of the proposed method is demonstrated by the classification results on the GML_DataSetA urban outdoor scene airborne point cloud dataset and the ISPRS Vaihingen 3D semantic marker reference dataset.