Current researches in subway train operation concentrate on timetable optimization and real-time tracking methods, which may be infeasible with disturbances in actual operation. To overcome stochastic negative effects caused by disturbances and realize energy-efficient train operation, we propose a comprehensive model to integrate train operation with real-time rescheduling. The proposed model focuses on minimizing the reward for both the total time-delay and energy-consumption with intelligent decision support. After designing policy, reward and transition probability, we develop an Intelligent Train Operation (ITO) algorithm based on Q-learning to calculate the optimal decisions. Simulation results with field data in Beijing subway Yizhuang Line demonstrate the effectiveness and efficiency of ITO approach.