Abstract: A method for intelligent traffic scheduling based on deep reinforcement learning, comprising: collecting flows in a data center network topology in real time, and dividing the flows into elephant flow or mice flow according to different types of flow features; establishing traffic scheduling models with energy saving and performance of the elephant flow and the mice flow as targets for joint optimization; establishing a DDPG intelligent routing traffic scheduling framework based on CNN improvement, and performing environment interaction; jointly inputting the three state messages as a state set into the CNN for training; setting an action as a comprehensive weight of energy saving and performance of each path under the condition of uniform transmission of flows in time and space, and selecting transmission paths for the elephant flow or the mice flow according to the weight; and designing reward value functions for the elephant flow and the mice flow.