This study delves into the application of a deep Q-Network (DQN) algorithm, which integrates strategies of Navigational Priority (NP) and Prioritized Experience Replay (PER), for intelligent path planning in maritime environments. Unlike conventional path planning algorithms, our optimized model autonomously explores and learns the patterns of the maritime environment without relying on manually constructed global maritime information. We have developed a maritime simulation environment based on the Gym framework to simulate and validate our improved DQN model. This model incorporates the mechanisms of Navigational Priority and Prioritized Experience Replay, enhancing the algorithm′s learning efficiency for critical decisions by adjusting the frequency of experience sample utilization during the learning process. Additionally, the introduction of a novel reward function has further strengthened the model′s adaptability and stability in addressing path planning issues. Simulation experiments demonstrate that our model significantly outperforms baseline methods in avoiding obstacles and finding optimal routes, showcasing notable generalizability and exceptional stability.