Abstract: With the acceleration of the global energy transition, pumped storage power stations (PSPS) have become a critical technology for stabilizing new power systems due to their regulation performance and economic advantages. However, traditional site selection methods relying on manual surveys suffer from inefficiency and limited coverage, hindering large-scale development. This study proposes an automated site selection methodology based on Digital Elevation Models (DEMs), which integrates terrain feature analysis and constraint-based hydrological modeling to construct a collaborative extraction framework for natural and semi-natural basins. The method innovatively incorporates key technologies such as depression identification, catchment area search, mainstream analysis, and dam site optimization, and combines dynamic thresholds to screen core parameters (e.g., effective storage capacity, water head, and length-to-height ratio), significantly improving site selection efficiency and engineering adaptability. Case studies demonstrate that this framework accurately identifies candidate reservoir basins, reduces survey costs, and resolves feasibility assessment challenges in artificial dam construction. The results provide a scientific basis for large-scale PSPS development.