Abstract: Pose estimation for space target is an essential prerequisite for various space missions. The key part of target pose estimation is establishing the mapping relationship between the two-dimensional feature and three-dimensional feature quickly and accurately. This task usually be decomposed into two sequential steps where are feature extraction and feature correlation. However, high dynamic illumination variation and the relative high-speed movement characteristics of the space target will significantly decay the reliability of image feature extraction which will affect the accuracy of feature matching and finally cause a drop in the accuracy of the pose estimation of the space target in the reality scene of space observation. To solve this problem, this paper proposes a pose estimation method based on semantic key point. In this work, the Hourglass Network is used to extract the key points containing semantic information end-to-end, thereby, the mapping relationship between the two-dimensional feature and three-dimensional feature is established. Then the target pose can be estimated utilized the EPnP algorithm. Experimental results demonstrate that the method proposed in this paper can reach a compromise between the accuracy and efficiency of the algorithm. The minimal error of pose estimation on the simulation data set can reach 0.83°, and the average error is better than the traditional method in the case of data degradation.