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With the research and development of mobile robots in various fields, people put forward higher requirements for the ability of mobile robot path planning. So as to solve the problems of slow convergence speed, many iterations, and large iteration space in the early stage of training when the traditional deep reinforcement learning algorithm is applied to the path planning of mobile robots in an unknown environment, an artificial potential field is added when the traditional DQN algorithm initializes the Q value. The attractive field of the algorithm is used as the prior information of the initial environment, so as to guide the mobile robot to move towards the target position, reduce a large number of invalid iterations caused by the environmental exploration in the initial stage of the algorithm, thereby reducing the number of iterations and speeding up the convergence speed. Using the pytorch framework in the grid map environment to verify the path planning effect of the improved DQN algorithm with the initial gravitational potential field. The simulation results show that the improved algorithm can quickly and effectively plan an optimal path from the starting point to the target point after generating a smaller iteration space and fewer iterations.