To address the challenges in autonomous driving environment perception — namely, that pure vision-based schemes are prone to misjudgment, existing 3D point cloud object detection algorithms suffer from insufficient feature representation due to the sparsity and inhomogeneity of point clouds, low detection accuracy in complex scenarios, and the fact that most algorithms only validate single-category objects, failing to meet the multi-category perception requirements of national standards — a feature-enhanced PointRCNN network (FE-PointRCNN) is proposed. To overcome the limitations of traditional MLP structures, which possess strong global feature extraction capabilities but weak local geometric feature capture, along with the high computational overhead of attention mechanisms, Enhanced Multi-Layer Perceptrons (E-MLPs) and Residual Channel Attention (RC-Attention) modules are designed. These modules are integrated to construct an improved E-PointNet++ feature extraction architecture that enables parallel multi-scale feature extraction and adaptive weighted enhancement of key channel features, thereby alleviating the gradient vanishing problem and enhancing feature discriminability. Comparative and ablation experiments are conducted on the KITTI and ONCE datasets. The results demonstrate that the proposed FE-PointRCNN achieves accuracy improvements over the baseline PointRCNN* for the Car, Pedestrian, and Cyclist categories and exhibits strong robustness on the complex-scene ONCE dataset. The proposed algorithm effectively improves point cloud feature extraction capability and multi-category object detection performance, providing valuable technical support for LiDAR-based perception systems in autonomous driving.