To address the issue that existing point cloud-based 3D object detection algorithms perform poorly on small object detection, a PointPillars 3D object detection algorithm based on an attention mechanism is proposed on the basis of the traditional PointPillars model. Firstly, the pillar feature network in the PointPillars model is improved by introducing a set of Efficient Channel Attention (ECA) mechanisms in the encoding network, allowing each Pillar module to better consider its local detailed geometric information, thereby significantly enhancing the feature representation capability of each Pillar module. Secondly, a Shuffle Attention module is introduced into the 2D convolutional downsampling module of the backbone network. By improving the diversity and effectiveness of feature representation, the model network focuses on important features while suppressing less important or redundant features, significantly enhancing the algorithm's feature extraction ability. Test results on the KITTI dataset show that the improved algorithm improves the 3D pedestrian detection accuracy by 4.04 percentage points, 3.43 percentage points and 1.93 percentage points for easy, moderate, and hard difficulties, respectively, significantly outperforming the original PointPillars network and enhancing the performance of small pedestrian object detection.