This paper addresses the core challenges in the 3D point cloud segmentation of the human auricle region, including the low distinguishability of weak-boundary features, the lack of anatomical priors in existing methods, the tendency of boundary features to be overwhelmed by background noise, and the weakening of supervisory signals. We propose GeoPriors-3DEarSeg, a precise geometric-priors-guided 3D point cloud segmentation network for auricle regions. The network incorporates complementary geometric features from three dimensions, normal vector orientation, local Gaussian curvature, and shape diameter function, to characterize the intrinsic geometric differences at the auricle boundary. A geometric-priors-guided QKV gated attention mechanism is designed to selectively enhance the expression of weak-boundary features. Additionally, we introduce a boundary-aware loss function, NVBLoss, which does not rely on extra annotations to strengthen the supervision of boundary features. The experimental results on a human ear dataset demonstrate that our method achieves a mean intersection over union (mIoU) of 0.9857, outperforms the comparison methods, and precisely segments of weak boundaries in the auricle region. This work provides a technical basis for accurate auricle-region point cloud segmentation and offesr methodological insights for future studies on the weak-boundary segmentation of other medical anatomical structures.