The purpose of the task scheduling strategy for multiple data centers is to allocate computing tasks to different servers in each data center to improve the resource utilization and energy efficiency. Therefore， a deep reinforcement learning-based integrated scheduling strategy for multiple data center was proposed， which is divided into two stages： data center selection and task allocation within the data centers. In the multiple data centers selection stage， the computing power resources were integrated to improve the overall resource utilization. Firstly， a Deep Q Network （DQN） with Prioritized Experience Replay （PER-DQN） was used to obtain the communication paths to each data center in the network with data centers as nodes. Then， the resource usage cost and network communication cost were calculated， and the optimal data center was selected according to the principle that the sum of the two costs is minimum. In the task allocation stage， firstly， in the selected data center the computing tasks were divided and added to the scheduling queue according to the First-Come First-Served （FCFS） principle. Then， combining the computing device status and ambient temperature， the task allocation algorithm based on Double DQN （Double DQN） was used to obtain the optimal allocation strategy， thereby selecting the server to perform the computing task， avoiding the generation of hot spots and reducing the energy consumption of refrigeration equipment. Experimental results show that the average total cost of PER-DQN-based data center selection algorithm is reduced by 3.6% and 10.0% respectively compared to those of Computing Resource First （CRF） and Shortest Path First （SPF） path selection methods. Compared to Round Robin scheduling （RR） and Greedy scheduling （Greedy） algorithms， the Double DQN-based task deployment algorithm reduces the average Power Usage Effectiveness （PUE） by 2.5% and 1.7% respectively. It can be seen that the proposed strategy can reduce the total cost and data center energy consumption effectively， and realize the efficient operation of multiple data centers.