A new 3D underwater AUV path planning method (IADQN) was proposed due to the low quality of the generated path and poor dynamic obstacle avoidance ability of AUV path planning methods in complex marine environments. In order to resolve the problem of insufficient obstacle recognition and avoidance ability of AUVs in unknown underwater environments, an adaptive potential field method was proposed to improve the efficiency of action selection of AUVs. In order to address the problem of low sample selection efficiency in the traditional deep Q network (DQN) experience replay strategy, a priority experience replay strategy was adopted to select samples with higher contributions to training from the experience pool to improve the efficiency of training. AUV dynamically adjusts the reward function according to the current state to accelerate the convergence speed of IADQN during training. Simulation results show that, compared with the DQN scheme, IADQN plans a time-saving and collision-free path efficiently in a real ocean environment; the AUV running time is reduced by 6.41 s, and the maximum angle with the ocean current is reduced by 10.39°.