The invention discloses a laser point cloud three-dimensional target detection model and method for a complex traffic scene, a three-dimensional encoder in the model is beneficial to the detection of long-distance targets and small targets, and sparse convolution and sub-manifold convolution can greatly improve the coding efficiency of voxel features. The residual structure of the two-dimensional encoder can keep more complete information, detection of a long-distance target and a small target is facilitated, meanwhile, the network is easier to optimize, feature extraction and receptive field expansion can be carried out on an original scale and a self-calibration scale through self-calibration convolution, more complete and rich features can be extracted. Useful feature expression is enhanced in the space direction and the channel direction through space attention and channel attention, and useless information is inhibited. The final detection precision of vehicles is 81.88%, the final detection precision of pedestrians is 47.82%, the final detection precision of riders is 69.81%, the average precision is 66.25%, the average precision is 9.9% higher than that of an existing VOXEL RCNN algorithm, 13.8 FPS is achieved on RTX 2080Ti display, and the detection precision and speed can meet the sensing requirements of intelligent vehicles in complex traffic environments.