To address the problems of low computational efficiency, excessive node exploration, poor safety, and non-smooth path in large-scale continuous obstacle environments, this paper proposes an improved A* algorithm. Firstly, eight types of two-layer 5-neighborhood are proposed to improve computational efficiency and enhance path smoothness, and boundary expansion and full-obstacle expansion methods are designed to resolve the deadlock caused by small neighborhood search. Secondly, a hierarchical heuristic function strategy is proposed. The strategy divides the search space into multiple layers and assigns different weights to the heuristic functions of each layer based on predefined thresholds, thereby reducing the number of explored nodes and further improving computational efficiency. Finally, a safety detection method is proposed to ensure that the generated paths maintain a safe distance from obstacles. Compared with five algorithms in different environments, simulation experiments demonstrate that the improved A* algorithm reduces running time by an average of 30.9%, increases path safety by an average of 14.7%. In addition, the improved A* algorithm generates paths with moderate smoothness and achieves better overall performance than the five compared algorithms. The proposed improved A* algorithm not only satisfies the requirements for safe and efficient path planning for mobile robots in large-scale continuous obstacle environments but also shows greater robustness in different environments. Even without secondary path optimization, the improved A* algorithm generates paths with relatively high smoothness.