In order to solve the problem of no-model path planning for UAV, a DQN path planning method based on potential function (PF) reward in the case of unknown environmental information is proposed. Establish a continuous state space of the drone in the environment, divide the 360? into several angles as the heading angle to establish the action space of the drone, design the target and obstacles to reward the potential function of the drone, and describe the difference more carefully. Carry out the simulation experiments. The results show that the PF-DQN algorithm can better realize the collision-free path planning of the UAV under the unknown environmental information, and the potential function reward can speed up the training speed of the UAV path planning network.