To address the issues of time-consuming manual tooth alignment, reliance on physician experience, and insufficient structural constraints in automated methods in traditional orthodontic treatment planning, this paper proposes an automated tooth alignment network model that integrates multi-source geometric information learning. First, this study uses tooth mesh data as input to construct a graph convolution-based network for extracting tooth geometric features, fully utilizing the topological connectivity and local geometric features of the tooth model to enhance the extraction of tooth morphological features. Then, an arch line prediction network is designed to guide the teeth to align orderly along the natural arch line by explicitly modeling the tooth alignment trajectory, thereby ensuring the anatomical rationality and aesthetics of the overall structure. Based on this, this study designs collision avoidance loss and arch alignment loss to reduce geometric conflicts between adjacent teeth and constrain the overall posture, making the alignment results more stable and realistic. To verify the effectiveness of the method, validation experiments were conducted on multiple sets of real dental arch data, and comparisons were made with mainstream methods such as tooth alignment network (TaligNet), parameterized spatial transformation network (PSTN), and Transformer-based network for tooth alignment (TANet). The results show that the proposed method improves the evaluation metrics to varying degrees, fully integrates global and local geometric information, improves tooth posture coordination and alignment smoothness, and provides efficient and standardized intelligent support for digital orthodontic treatment planning.