To address the issue of poor performance in detecting small objects by current 3D object detection algorithms based on the combination of point clouds and voxels, this paper proposes a 3D object detection algorithm based on a multi-attention mechanism (MA-RCNN). First, a channel attention mechanism is introduced in the PV-RCNN baseline algorithm to process the bird's-eye view features after compressing voxel features, aiming to propagate spatial information to feature channel levels. Second, a spatial attention mechanism is introduced to amplify locally important information, thereby enhancing the expressive power of the features. Then, in the refined candidate box network, a point cloud self-attention mechanism is designed to construct relationships between key points, thus enhancing the algorithm's understanding of spatial structures. Experimental results on the KITTI dataset show that compared to the baseline algorithm, MA-RCNN improves the mean average precision for small objects such as pedestrians and cyclists by 3.20 percentage points and 1.64 percentage points, respectively, demonstrating its effectiveness. Compared to current mainstream 3D object detection algorithms, MA-RCNN still achieves better detection performance, verifying its advanced nature. The MA-RCNN is deployed on the real vehicle hardware platform for online testing, and the results verify its industrial value.