Becaused of the actual navigation requirements of unmanned surface vehicles, the planned path should meet the criteria of smoothness and economy. Therefore, a novel path planning strategy based on an improved crow search algorithm combining straight lines and circular arc turns is proposed. A new path fitting method is introduced to optimize the number of turning points and address the issue of arc transition at turning points. This method overcomes the problem of frequent direction adjustments caused by B-spline curve paths for unmanned surface vehicles, while ensuring that they can achieve steering while maintaining a stable speed, thereby improving navigation stability and economy. Based on this, an improved crow search algorithm is introduced to optimize the location of path turning points. The improvement of the algorithm is mainly reflected in three aspects: the use of a reverse learning strategy to optimize the quality and the diversity of the initial population, the proposal of a dynamically changing awareness probability to improve the global search ability of the initial segment and the local search ability of the final segment of the algorithm, and the utilization of the Levy flight strategy to improve the directionality and the effectiveness of the search. The simulation results show that the proposed new path fitting method is superior to the B-spline curve fitting method and the straight line segment fitting method. Building on this fitting method, the improved crow search algorithm, the standard crow search algorithm, the differential evolution algorithm, and the genetic algorithm are used to optimize the location of the path turning point. Iterative calculation and variance analysis results demonstrate that the proposed improved crow search algorithm exhibits higher convergence accuracy and robustness compared to the other three algorithms, effectively addressing practical problems in unmanned surface vehicle path planning.