Abstract: With the widespread use of mobile devices and the advances in positioning technologies, trajectory data have become increasingly accessible, enabling applications in user behavior analysis and intelligent services based on trajectory data. Spatiotemporal trajectory pattern mining plays an important role in applications such as urban traffic optimization and tourist route planning, but large-scale similarity computation remains a major challenge. To address this issue, a spatiotemporal trajectory pattern mining framework for high-concurrency trajectory streams is proposed. The framework consists of two components: index-tree group discovery and pattern mining. In the first component, an index optimization strategy is developed to identify spatially neighboring groups efficiently. In the pattern mining stage, a vehicle ID-based partition algorithm is employed to improve mining efficiency. The proposed framework is validated on real-world datasets. Experimental results show that the proposed method reduces computational time by an order of magnitude while maintaining comparable pattern mining performance.