3D vehicle detection is a key problem in automatic driving scene, which involves 3D object detection and 3D object classification. Current 3D detection and classification networks treat all input point cloud data equally. However, in the actual detection process, the importance of different points in the point cloud for detection may not be the same. In order to get better detection results, attention mechanism is introduced to get the weights of the features of different points, so that the features of some points can get more attentions in regression. Experiments show that the model has higher accuracy than the existing methods while maintaining real-time efficiency.