Most existing three-dimensional (3D) object detection methods are prone to missing distant targets and exhibit poor detection accuracy for small object categories. Hence, this paper proposes a 3D object detection algorithm based on a graph neural network and dynamic sampling. In the candidate box generation phase, a graph feature enhancement module strengthens the semantic information among pillar features to generate high-quality pseudo-images. In the key point sampling phase, a dynamic sampling strategy is designed to not only improve sampling efficiency but also increase the proportion of foreground points. Additionally, a dynamic farthest voxel sampling method ensures the uniform distribution of key points. In the candidate box refinement stage, a multi-scale graph pooling module extracts rich local features. Finally, an adaptive module integrates local information with key point data to enhance feature representation. Experimental results based on the KITTI dataset demonstrate that the proposed algorithm achieves mean average precision (mAP) scores of 56.08% for pedestrians and 77.65% for cyclists, representing improvements of 12.29 and 10.04 percentage points, respectively, over those of the baseline PointPillars algorithm. Moreover, the proposed algorithm exhibits higher precision in detecting distant targets, thus validating its effectiveness in both long-range target detection and small object category detection.