Original name record: Chongqing University

The present invention relates to an economic driving strategy for hybrid electric vehicles in complex traffic scenarios based on deep reinforcement learning, belonging to the field of new energy vehicles. The method comprises: constructing an interactive multi-lane multi-traffic signal training scenario: describing longitudinal motion of vehicles in the training scenario using vehicle kinematic models; simplifying lane-changing processes of vehicles into transient states; controlling surrounding vehicles through rule-based decision models to establish environmental interactivity; building a maximum entropy deep reinforcement learning-based decision model containing: state space, action space, reward function, policy model critic model, and experience replay buffer; establishing safety constraints for the target vehicle, including: longitudinal acceleration safety constraints, lateral lane-changing decision safety constraints, preventing collision risks and traffic regulation violations; training the maximum entropy deep reinforcement learning-based decision model. The invention enhances fuel economy of autonomous vehicles through deep reinforcement learning techniques.