Aiming at the problem of 3D irregular point cloud format and uneven density, a fusion of multi attention mechanism and pointrcnn network is proposed for 3D point cloud target detection. This experiment mainly improves the pointrcnn two-stage network respectively. Firstly, the channel attention and spatial attention mechanism are serially input to the distribution of each network layer in the first stage by adjusting and normalizing in batch to further quickly identify three-dimensional features; Secondly, the cross position attention mechanism is introduced into the second stage network to avoid the position deviation of the cross path, so as to further refine the three-dimensional target position for feature extraction. The experimental results on Kitti data set show that compared with pointrcnn detection network, the improved network improves the average mean accuracy (map) of car and pedestrian tests by 1.2% and 1.9% respectively. Therefore, the improved method not only solves the problems of irregular point cloud format and uneven density, but also ensures the detection accuracy.