The train operation and control system is a safety-critical system whose software correctness directly affects railway operational safety. Traditional test case design relies on expert experience, leading to low coverage, poor repeatability, and insufficient completeness. To address these challenges, we propose a model-driven automatic test case generation method for the train operation and control system. A multi-view modeling framework is established, including the system function model, the environment model, the operational scenario model, and the line data model. Test cases are automatically generated by traversing the model space based on the predefined coverage criteria. To overcome the limitations of conventional open-loop modeling, a closed-loop modeling approach is proposed, where the environment constraints, the scenario sequences, and the line data constraints are integrated as additional solvable conditions. A constraint-satisfaction-based line data solver is proposed to select test data that meet the specific scenario requirements. The proposed method is applied to the Chinese Train Control System Level 3(CTCS-3 )level train operation and control system for functional validation. Experimental results show that the proposed approach achieves 100% state coverage and 96% transition coverage on a typical station scenario, and reduces manual test case design effort by 65%.