Aiming at the limitations of dynamic graph convolutional neural network (DGCNN) on aggregating neighbor point information, a dynamic graph convolutional neural network based on enhanced feature fusion (EFF-DGCNN) model is proposed and is used for airborne LiDAR point cloud classification. The model presents the feature enhancement module and the feature fusion module based on DGCNN, which can be applied to the classification of original 3D point clouds. Firstly, the local and global features of the original point clouds are obtained by edge convolution based on DGCNN. Then, the global features are integrated into the local features of each layer to enhance the local features, so as to highlight the importance of different features of point clouds and make the network pay more attention to the features conducive to classification. Finally, different enhanced local features are fused to obtain deep features. The fused enhanced local features are used for classification of airborne LiDAR point clouds. In order to verify the classification performance of the proposed model, experiments are conducted on the GML_DataSetA dataset and ISPRS dataset. It is demonstrated that compared with DGCNN, the proposed EFF-DGCNN model has better classification ability and can better distinguish point clouds with similar structures.