In this work, an optical-flow-based pose tracking method with long short-term memory for known uncooperative spacecraft is proposed. In combination with the segmentation network, we constrain the optical flow area of the target to cope with harsh lighting conditions and highly textured background. With the introduction of long short-term memory structure, the proposed method can maintain a robust and accurate tracking performance even in a long-term sequence of images. In our experiments, the pose tracking effects in the synthetic images as well as the SwissCube dataset images are tested, respectively. By comparing with the state-of-the-art pose tracking frameworks, we demonstrate the performance of our method and in particular the improvements under complex environments.