Lidar data is widely used in 3D target detection tasks due to its geometric characteristics. Due to the sparsity and irregularity of point cloud data, it is difficult to achieve the balance between the quality of feature extraction and the speed of reasoning. In this paper, a three-dimensional target detection algorithm based on body-column feature coding is proposed. Based on Pointpillars network, the Teacher-Student model framework is designed to distill the regression frame scale, increase distillation loss, optimize the training network model, and improve the quality of feature extraction. In order to further improve the model detection effect, the positioning guidance classification item is designed to increase the correlation between classification prediction and regression prediction, and improve the object recognition accuracy. The improvement of this network does not introduce additional network embedding. The experimental results of the algorithm on the KITTI dataset show that the average accuracy of the reference network in 3D mode is improved from 60.65% to 64.69%, and the average accuracy of the aerial view mode is improved from 67.74% to 70.24%. The model reasoning speed is 45 FPS, which meets the real-time requirements while improving the detection accuracy.