In view of the problems such as low efficiency of path planning, high risk of collision, and excessive map exploration caused by fixed parameters in the scene of varying complexity of real water area maps, such as rivers and coasts, a path planning algorithm for unmanned ships based on dynamic potential field guidance of local map complexity is proposed. The algorithm can significantly improve the planning performance and safety in complex waters by dynamically adjusting parameters through real-time perception of the environment. Firstly, according to each extended node, the obstacle collision detection is implemented in real time, and the local map complexity of the node is calculated. Based on this complexity, the gravitational strength and repulsive strength of the potential field are dynamically adjusted, the hybrid weights of random exploration and potential field guidance in the node generation strategy are adjusted adaptively, and the switching of the three-level safety step size is used to solve the adaptation problem of the traditional fixed parameters in the complex overlapping waters in the actual environment. Meanwhile, a compound navigation potential field integrating target gravity, obstacle repulsion, and boundary repulsion is constructed to solve the problem of obstacle avoidance and welt safety balance in narrow waterways and improve the safety of path planning. Then, the redundant nodes are removed by pruning, and the key turning points are optimized by stretching. The cubic B-spline smooth path is used to ensure the curvature continuity, improve the path feasibility, and meet the requirements of unmanned ship mission execution. Through electronic chart simulation and unmanned ship experiment, it shows that the proposed algorithm maintains advantages in planning time, path length, sampling points, and path feasibility. Compared with the improved algorithm DVSA-RRT, in a complex environment, the planning time is shortened by 87.27%, the path length is shortened by 21.6%, and the sampling points are reduced by 78.53%, which improves the efficiency of path planning, reduces the path planning space, and meets the requirements of the unmanned ship mission.