Accurate tooth segmentation in 3D dental jaw data is crucial for diagnosis and treatment planning. Aiming at the pain points of existing algorithms, such as low classification and segmentation accuracy and high video memory consumption under conditions of unclear tooth boundaries, crowding, misalignment, and tooth loss, a two-stage tooth classification and segmentation model with an attention mechanism to enhance feature extraction was proposed Due to the large input of 3D point cloud models, which led to a large amount of computation, to reduce video memory consumption, teeth were located by detecting 2D images and projects 3D point clouds into 2D images. To address the problem of low classification accuracy of existing methods in cases of tooth loss and misalignment, a tooth feature enhancement loss module (FELM) constructed by Transformer was added on the basis of the existing algorithm as the backbone network to strengthen the correlation learning between data points in each tooth region, so as to improved the classification accuracy of detection. In the second stage, after detecting the 2D tooth region, the corresponding single-tooth point cloud data on the cropped 3D dental jaw model needed to be segmented. To address the problem that EdgeConv can only learn local features, a global channel relation feature (GCRF) module based on the attention mechanism was added to enhance the fusion learning of global features, and an EdgeConv-GCRF segmentation model was obtained to segment a single tooth. Through experiments on the Teeth3DS dataset, the proposed method achieves the mean accuracy (mAcc), mean intersection over union (mIOU), and mean Dice score (mDice) of 0.860 8, 0.824 9, and 0.866 6, respectively. Compared with other existing methods, it significantly improves the classification and segmentation accuracy, can efficiently complete the segmentation task, and has great economic and social value.