With the development of machine learning, deep learning has been widely applied in industrial manufactory, and reinforcement learning also has a good performance in lots of games. This paper presents a method based on deep reinforcement learning on goalkeeper strategy of wheeled soccer robot which makes the robot goalkeeper can defend the attacker shoots the soccer toward the goal and presents an effective training method that can make robot goalkeeper has a high effectivity in learning the strategy. We adopt the Soft Actor-Critic (SAC) which is one of a deep reinforcement learning algorithm. With the Gazebo simulator and SAC, the robot goalkeeper can explore the environment and learn the strategy in the simulated environment, and enable the robot goalkeeper stable and effectively complete the specified tasks by designing neural network architecture. In the section of the training method, the idea of this paper creates a useful reward function and using progressive learning. By increasing the complexity of the environment step by step, the training can be more stable, and reduce the training time. According to the method presented in the paper, by dividing the field into several areas to test the result, the robot goalkeeper has a good performance in its defending rate that proofs the robusticity of the present method.