With the advancement of urbanization and digital twin city development, urban 3D data are characterized by large volume, heterogeneity, and structural complexity. Traditional spatial data management methods face limitations in hierarchical organization, retrieval efficiency, and redundancy control, and the lack of a unified spatial coding system hinders multi-source data integration. This paper proposes a method for urban 3D data management and representation based on BeiDou grid coding and adaptive voxel modeling. The method converts point cloud data from local coordinates to the 2000 National Geodetic Coordinate System, applies 36-bit 3D BeiDou grid coding, performs adaptive octree voxel partitioning based on point cloud density, elevation variation, and class entropy, and binds spatial, geometric, and semantic attributes at the voxel level. Using the SensatUrban dataset, the method is compared with fixed-resolution voxel modeling, latitude-longitude indexing, and R-tree indexing in terms of voxel quantity, data storage, and retrieval time. Results show that it reduces voxel count by 28.1% and storage volume by 13.6% while maintaining high-precision representation, and the BeiDou grid-based indexing significantly improves query efficiency and stability. The proposed approach balances visualization quality and computational efficiency, providing an effective solution for large-scale urban 3D data management.