In order to overcome the problem that existing airborne methods for LiDAR point cloud classification have difficulties in obtaining high classification accuracy and reducing processing time simultaneously, a method using transfer learning for classifying airborne LiDAR point clouds was proposed. First, normalized height, intensity, and normal vector were calculated for each LiDAR point, by setting different sizes of neighborhood, and multi-scale point cloud feature images were generated by utilizing the proposed feature image generation strategy. Subsequently, a pre-trained deep residual network was employed to extract multi-scale deep features from the generated multi-scale feature images. Finally, a neural network model containing only two fully connected layers was constructed to achieve efficient training, and each point cloud was classified by the trained neural network model. Experimental results of two ISPRS benchmark airborne LiDAR point cloud sets demonstrat that the proposed method requires less training time, and the overall classification accuracy obtained by the method is 89.6%, which is 4.4% higher than the best classification result reported on the ISPRS official website. The classification result can provide reliable information for further processing and application of point cloud.