Current path planning algorithms for mine cars generally suffer from low search efficiency and excessively long planned paths when navigating complex mining terrain. Developing highly efficient and precise path planning methods has thus become a critical issue requiring urgent resolution. An innovative two-layer transportation path planning scheme was proposed. The upper layer employed the improved A^* algorithm to initially plan the paths between nodes, while the lower layer utilized the improved sparrow search algorithm (ISSA) for overall path optimization. This ensured the minimization of energy consumption and cost for the overall path, aiming to achieve efficient and precise mining transportation path planning. The results show that the two-layer transportation path planning scheme based on the improved A^* algorithm and ISSA not only realizes the minimization of path energy consumption, but also minimizes the path cost, effectively enhancing the efficiency and safety of path planning. In the path planning process, the proposed scheme by the research institute will not be affected by obstacles, there will be no situation of taking a detour, and it can reach the destination along the shortest path. It has excellent obstacle avoidance and shortest path planning capabilities. Moreover, in the three-dimensional topographic map of the mine with complex terrain, it can quickly solve the target function value with lower energy consumption and lower path cost. The shortest transportation distance obtained by the two-layer transportation path planning scheme based on the improved A^* algorithm and ISSA is only 3 125.36 m. The cargo damage cost, transportation cost, and carbon emission cost are 1 028.63 yuan, 3 968.57 yuan, and 335.18 yuan, respectively, and the transportation time is only 1 836.39 s. All these indicators are lower than those of the mine transportation path planning scheme based on the genetic-ant colony reinforcement learning algorithm.