Aimed at the problem that the traditional tree species classification method based on LiDAR (light detection and ranging) data is difficult to directly and comprehensively use the 3D structure information of the point cloud, a tree species classification method of airborne LiDAR data based on 3D deep learning was proposed. This method directly abstracts high-dimensional features from 3D data without converting point clouds into voxels or two-dimensional images. Taking the airborne LiDAR data of white birch and larch in Saihanba National Forest Park as the research object, data filtering was performed to remove noise and ground points; the point cloud distance and improved watershed segmentation method were used to extract the individual wood and make a data set. Finally, a deep neural network composed of a weight-sharing multi-layer perceptron, a max pooling, a fully connected layer and a softmax classifier was established, which can extract the high-dimensional features of trees automatically and realize tree species classification. The experimental results show that the overall classification accuracy rate is 86.7%, the kappa coefficient is 0.73, the optimal feature dimension is 1 024, and the most advantageous point density is 2 048. Compared with the method projecting individual tree point cloud to a two-dimensional view, this algorithm provides higher classification accuracy, and can reduce the calculation cost effectively and improve work efficiency.