Dynamic path planning of surface boats during navigation is of great significance to ensuring navigation safety. In view of the multi-target dynamic path planning of surface ships, puts forward an improved IRRT* algorithm, fully considering the unique kinematic constraints of surface ships and the size of its hull, obstacles, the two-way search mechanism, search at the starting point and set a grading strategy, significantly improve the efficiency of path planning, and can better solve the optimal efficiency of multi-target path planning. Second, the path search process is optimized by introducing dynamic KD tree for nearest neighbor search, and reconstructing the KD tree regularly reduces the retrieval depth of the query nodes to further improve the search efficiency. Finally, the design is actually considering the cost function of ship turning angle and energy consumption, fusion the idea of artificial potential field method, introducing the gravitational field and repulsion gain coefficient as a local obstacle avoidance strategy, and finally adopt the adaptive third-order B spline curve optimization path, improve the smoothness of the path and real-time obstacle avoidance ability of water boats. Through simulation experiments and actual offshore tests in the Python environment, the results demonstrate the advantages of the algorithm in computing time, path length, performance of collision avoidance, and number of path turns. The research results provide a new idea for efficient path planning in complex waters, and contribute to the development of autonomous navigation technology of surface ships.