In the domain of transmission lines design, the use of 3D digital design technology can significantly improve the fine-grained terrain division. However, the fine-grained terrain division will make the dimension of terrain grid matrix increase exponentially, which leads to the dimension disaster in the process of path planning. In order to solve the dimension disaster caused by fine-grained terrain division, a digital transmission line path planning method based on hierarchical reinforcement learning is studied. Firstly, a three-dimensional digital cloud platform for transmission lines is established. Then, different scales are used to resample the terrain data, and the original terrain is reconstructed into two layers of coarse-grained and fine-grained grid map. Then, the hierarchical reinforcement learning based on MAXQ algorithm is used for path planning, so as to solve the dimension disaster problem caused by fine-grained grid cells, while maintaining the advantage of accuracy. The practical study shows that the proposed method can still keep convergence when the accuracy of terrain division is improved and the traditional method cannot converge. Compared with the traditional method, the unreasonable crossing area is less and the cost of path planning can be reduced.