In order to improve the visual navigation ability of mobile robots without maps and improve the success rate of visual navigation, a visual navigation model of mobile robots is proposed that integrates long short term memory (LSTM) and proximal policy optimization (PPO) algorithms. Firstly, the model integrates LSTM and PPO as a network model for visual navigation. Secondly, a new reward function is designed to train the target through factors such as the action of mobile robots, the distance between the robots and the target, and the running time of robots. Finally, the RGB-D image obtained from the first perspective of mobile robots and the polar coordinates of the target in mobile robots coordinate system are used as the model input, and the continuous motion of mobile robots is used as the model output to realize the task of end-to-end visual navigation without maps, and the new target that has not been trained is reached according to the model inference. Compared with the pre-order algorithms, the model has an average increase of 17. 7% in the navigation success rate of the old target and 23. 3% of the new target in simulated environments, which has better navigation performance.