To address the issue of poor detection performance for small objects in current light detection and ranging(LiDAR) 3D object detection algorithms, this study proposes a 3D object detection algorithm that integrates multi-scale features and grouped convolutions. First, this algorithm uses PV-RCNN++ as the baseline network and employs a 3D multi-scale feature network to fuse features at different spatial resolutions, resulting in 3D feature volumes that contain diverse spatial semantic information. This enhances the model's ability to extract features of the model for objects of different scales and improves detection performance. Then, different channels are grouped on 2D feature maps, and each group is convolved with convolution kernels of different sizes. This produces 2D features with multiple receptive fields of different sizes. Finally, the SimAM attention mechanism is utilized to increase the weight of foreground point features in the second stage, allowing these points to play a more significant role in the refinement of candidate boxes. The experimental results on the KITTI dataset show that the proposed algorithm outperforms current mainstream object detection algorithms. Compared to the PV-RCNN++ baseline network, the detection average precision of pedestrians in easy, moderate, and hard scenarios increases by 3.12, 3.95, and 3.70 percentage points, respectively, and that of cyclists in easy, moderate, and hard scenarios increases by 4.45, 3.52, and 3.08 percentage points, respectively.