Jump point search is a fast graph search algorithm widely used in path planning. However, the complexity of the hopping point search process and the excessive number of node extensions can lead to low search efficiency. To address these limitations, this paper proposes a global path planning strategy based on an improved JPS algorithm. The proposed approach includes a method to reduce redundant node extensions and introduces a safe and smooth path generation strategy. First, a directional priority ranking is introduced, where the search directions are adjusted to prioritize movement toward the goal. Nodes are then expanded sequentially according to this ranking to search for jump points more efficiently. Second, several path optimization strategies are combined, including safe node updating, redundant node elimination and path smoothing to ensure the safety and smoothness of paths. The safe node update strategy reduces the dangerous paths, the redundant node elimination strategy effectively reduces the path length, and the path smoothing strategy improves the smoothness of the paths by three times quasi-uniform B-spline curve processing. Finally, the performance of the improved algorithm is verified through simulation and real scenarios. The simulation results show that the improved JPS algorithm reduces the search time by 19.0% and 99.92% in complex environments compared to the traditional JPS algorithm and A* algorithm, respectively, and the number of expansion nodes of the improved-JPS algorithm is reduced by 56.9% compared to the JPS and 98.9% compared to the A* algorithm. 98.9%. In more complex real-world environments, tests conducted on a ROS-based intelligent vehicle demonstrate search time improvements of approximately 20.5% over A* and 28.0% over JPS. These results confirm that the proposed Improved-JPS algorithm significantly enhances the efficiency and safety of path planning in complex scenarios, validating its effectiveness and superiority.