Recently, driven by innovation of GPS technology, mobile location-based applications were a development of blowout. Trajectory data which contains mining valve is widely distributed and large-scale. How to organize trajectory data on a global scale and support efficient range queries becomes a problem. This paper presents a framework of adaptive Geohash-Tree based on Secondo database to organize the worldwide and large-scale trajectory dataset. In our framework, according to the Geohash code, different trajectory datasets will be covered by the deepest Geohash grid, and then we take this Geohash grid as root node to generate adaptive Geohash-Tree. In order to speed up queries to locate the corresponding index, we design prefix-sharing tree on the basis of the feature of Geohash. Adaptive Geohash-Tree is a spatial index based on grid. It can divide the space according to the track density by adopting a variety of strategies which improves the efficiency of range query. Meanwhile, we design the algorithm of incremental insertion and update for the supporting of real-time update of trajectory data. Furthermore, adaptive Geohash-Tree has been migrated into Secondo database. By taking advantage of database, management and query of trajectories can be easier. The experiment results verify that our approach in several aspects such as range query and occupied disk size performs better than the commonly applied R-Tree.