To address the limitations of slow convergence, excessive path turning points, and susceptibility to local optima in the path planning of unmanned surface vehicles (USVs) in complex environments, this paper proposes a global path planning method based on an improved Ant Colony Optimization (ACO) algorithm. The method employs the grid-based approach for environmental modeling. By integrating an artificial potential field mechanism and a node-degree-weighted heuristic function, the convergence speed and search efficiency are significantly enhanced. A linearly decreasing adaptive pheromone evaporation rate is introduced to further accelerate convergence. Additionally, an optimal–worst ant pheromone updating strategy is applied to avoid falling into local optima. A path optimization strategy based on connectivity principles is also proposed, effectively reducing the number of turning points and eliminating redundant nodes. Experimental results on 2D grid maps of varying sizes demonstrate that, compared with the traditional ACO algorithm, the proposed method generates shorter paths with fewer turns and achieves faster convergence.