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Aiming at the limitation of mobile robot navigation in complex and dynamic environment, a deep reinforcement learning method combining deep learning and reinforcement learning is adopted. In this study, the image of the simulation environment built under the OpenCV platform was used as input data, which was input into the convolutional neural network model created by TensorFlow for processing, in which the robot"s action state information was extracted, and the optimal navigation strategy was obtained by combining the decision-making ability of reinforcement learning. The simulation results show that after the training with the method of deep reinforcement learning, the mobile robot can still realize the efficient and accurate navigation from the random starting point to the random ending point when some scenes change in the environment.