To meet the requirements for precise positioning of spacecraft components during space missions， this paper proposes a spacecraft component tracking algorithm based on a Siamese neural network. The proposed approach solves the common problem of confusing similar components. First， the spacecraft component tracking problem was modeled by training with data via the neural network； the Siamese network was designed by improving the AlexNet network. A large public dataset GOT-10k was used to train the Siamese network. Stochastic gradient descent was then used to optimize the network. Finally， to eliminate the positioning confusion occasioned by the resemblance of similar parts of the spacecraft， a tracking strategy combining motion sequence characteristics was developed to improve the tracking accuracy. The spacecraft video data published by ESA was used to test the proposed algorithm. The experimental results show that the proposed algorithm， without using spacecraft related data for training， achieves 57.2% and 73.1% of the intersection ratio of the tracking results between the cabin and solar panel， and the speed reaches 38 FPS. This demonstrates that the proposed method can meet the requirements of stable and reliable tracking of spacecraft components with high precision and strong anti-interference.