In order to improve the economy and safety of ship navigation path in actual sea environment, this paper proposes a ship global path planning method with an improved Deep Q-Network (DQN) algorithm.

First, a prioritized experience replay （PER） mechanism is introduced to the DQN to give higher weights to important samples and improve learning efficiency. Next, its network structure is improved through a duel network and noise network, enabling it to evaluate the values of specific states and actions more accurately and generalization capabilities.

An experiment is carried out in the marine environment near Manila, and the results show that compared with the A^* algorithm and DQN algorithm, the improved algorithm reduces the path length by 1.9% and 1.0% respectively, and the number of turning points by 62.5% and 25% respectively.

It is verified that the improved DQN algorithm can plan the effective path more economically and rationally.