Abstract: To solve the path planning problem of mobile robot in static unknown environment, a new path planning method was proposed which combined the deep auto-encoder with the Q-learning algorithm, namely the DAE- Q path planning method. The deep auto-encoder processed the raw image data to get the feature information of the environment. The Q-learning algorithm chose an action according to the environmental information and the robot moved to a new position, changing the surrounding environment of the mobile robot. The robot realized autonomous learning through the interaction with the environment. The system processed raw image data and extracted the image feature autonomously by combining the deep auto-encoder and the Q-learning algorithm, and the autonomy of the system was improved. In addition, an improved Q-learning algorithm to improve the system’s convergence speed and shorten the learning time was utilized. Experimental evaluation validates the effectiveness of the method.