Abstract: To address the issue of existing deep learning-based segmentation methods for dental models, which overly rely on coordinate features and overlook the differences and complementary nature of geometric and coordinate features, a segmentation network based on attention mechanism and neighbor enhancement is proposed. First, an attention-enhanced module is employed to enhance the neighborhood of input features through different encoding schemes. It designs a differential attention mechanism and a self-attention mechanism to extract comprehensive contextual features of the mesh by considering the properties of geometric and coordinate features separately. Next, a complementary pooling function is constructed to fuse maximum pooling, attention pooling, and average pooling, which promotes the complementary interaction among different features and enhances the robustness of the segmentation network. Finally, an adaptive attention module is embedded to balance the numerical differences among multi-scale features for effective mesh segmentation. Experiments conducted on a real patient dataset of dental models demonstrate that the proposed method outperforms state-of-the-art deep learning methods in dental model segmentation.