Aiming at the problems of high subjectivity and low efficiency of the current traditional diagnosis of malocclusion, the article designs GraphTeeth, an automated diagnosis method of malocclusion based on graph neural network. GraphTeeth combines the architectural advantages of graph neural network, which is able to efficiently capture the topological information of the teeth and their surrounding structures. By modeling the position, morphology, and interrelationships of teeth as graph structures, finer local and global features are learned using a message passing mechanism between nodes. In the experimental phase, a large dataset containing various types of malocclusion cases was used to train and test GraphTeeth.The experimental results show that GraphTeeth significantly outperforms existing target detection methods in the key performance metrics. On the mAP metric, GraphTeeth achieves 43.45%, which is a significant improvement over traditional target detection algorithms such as Mask R-CNN at 32.26%, EfficientDet at 38.73%, and DETR at 25.05%. In addition, for specific types of malocclusions-such as fixed orthodontic appliance fitting-GraphTeeth achieves an accuracy of 91.28%, while the recognition rate for healthy teeth reaches 83.91%. The results suggest that GraphTeeth is able to provide faster, more accurate and objective diagnosis, providing reliable support for orthodontic treatment.