The invention relates to an intelligent locomotive operation method and system based on deep reinforcement learning. The system comprises a data source module, a locomotive operation environment learning module, an evaluation mechanism learning module and a control strategy learning module, the data source module provides needed data input for the locomotive operation environment learning module and the evaluation mechanism learning module, and the locomotive operation environment learning module and the evaluation mechanism learning module output an obtained specific operating environment and a reward function value to the control strategy learning module. On the basis of a deep reinforcement learning algorithm, a locomotive operation environment model takes real-time evaluation of the locomotive operation action as feedback information, by rewarding or punishing the current operation action, a reward function is fed back to the control strategy to serve as a reward evaluation value, and the control strategy is combined with the operating state to iteratively update and optimize the strategy. Accordingly, intelligent and optimized locomotive operation can be better achieved, and artificial participation is greatly reduced.