A semantic network-based network search algorithm is proposed to address the problems of slow planning speed and high memory occupation of raster map-based path planning techniques in the face of large maps and high-resolution maps. Firstly, a semantic partitioning network is used to pre-sample the raster map, secondly, the optimal path range is formed by widening the optimal path through imagery expansion to improve the robustness of the algorithm, finally, the feature map of the semantic network is used to guide the planning of the search algorithm, which speeds up the path planning of the high-resolution raster map. Experimental simulations show that the network search algorithm reduces the time by an average of 72. 5%, the number of traversal points by an average of 51. 6%, and the path length by an average of 0. 73% compared to the traditional search algorithm, and the network search algorithm can effectively speed up the path search and reduce the memory occupation.