Con el desarrollo vigoroso de Internet, el tráfico de red de los centros de datos ha explotado, y al mismo tiempo, el consumo de energía de red de los centros de datos también ha aumentado rápidamente. Los algoritmos de enrutamiento existentes solo realizan la optimización de enrutamiento a través de la Calidad de Servicio (QoS) y la Calidad de Experiencia (QoE), lo que ignora el consumo de energía de las redes de centros de datos. Aiming at this problem, this paper proposes an Ee-Routing algorithm, which is an energy-saving routing algorithm based on deep reinforcement learning. First, our method takes the energy consumption and network performance of the data plane in the software-defined network as the joint optimization goal and establishes an energy-efficient traffic scheduling scheme for the elephant flows and the mice flows. Then, we use Deep Deterministic Policy Gradient (DDPG), which is a deep learning framework, to achieve continuous and energy-efficient traffic scheduling for joint optimization goals. The training process of our method is based on a Convolutional Neural Network (CNN), which can effectively improve the convergence efficiency of the algorithm. After the algorithm training converges, the energy-efficient path weights of the elephant flows and the mice flows are output, and the balanced scheduling of routing energy-saving and network performance is completed. Finally, the results show that our algorithm has good convergence and stability. Compared with the DQN-EER routing algorithm, Ee-Routing improves the energy saving percentage by 13.93%, and compared with the EARS routing algorithm, Ee-Routing reduces the delay by 13.73%, increases the throughput by 10.91%, and reduces the packet loss rate by 13.51%.