In order to realize the operation of components without cooperation markers in on-orbit services, it is necessary to segment the area of the relevant components finely and then track them stably. For the refinement segmentation of components, firstly, the instance segmentation network, Mask RCNN, is trained on the spacecraft component instance segmentation dataset, and secondly a mask refinement module is added to its mask segmentation branch to optimize the component segmentation results. As to component tracking, a hierarchical weighted quintuple loss based on the Quit_trihard loss is proposed to train a re-identification network on the component re-identification dataset, and then the re-identification network trained before is embedded into the Deep OC SORT tracking algorithm for stable component tracking. The experimental results show that after mask optimization, the component segmentation accuracy of the relevant instance segmentation algorithm on the component segmentation test set can be improved to 84.90 mAP; by using the improved loss, the identification success rate on the component re-identification test set is improved to 76.86%, and the tracking success rate of the correlation tracking algorithm on the component tracking test set is improved to 89.38%. Therefore, the method proposed in this paper can basically satisfy the fine segmentation and stable tracking of spacecraft components.