The invention discloses an unmanned ship path planning method based on deep reinforcement learning and considering marine environment elements. The method comprises the basic steps of S1, interpolating wind, wave and flow data of a target sea area, and adding obstacle information, starting point information and end point information, S2, evaluating the maximum value of the wind wave flow borne bythe unmanned ship by using a Bayesian network, S3, reorganizing the AIS data of the target sea area into a training network to obtain an optimized experience pool and preliminary network parameters, S4, respectively inputting the unmanned ship state feature vectors into a deep reinforcement learning module for algorithm iteration, updating network parameters, and outputting actions,S5, iterating the unmanned ship to run for 15 seconds each time, and updating the data when the cumulative time reaches 1 hour,and S6, when the unmanned ship arrives at the target point, ending iteration, and outputting a path. According to the method, the influence of the marine environment factors on the navigation of the unmanned ship is fully considered, the actual long-range navigation condition of the unmanned ship is better met, the environment factors and obstacle information can be considered at the same time under the severe sea condition of the unmanned ship, and high-quality safety is obtained.