To solve the few-shot satellite components domain detection, this paper proposes an object detection algorithm for adaptive migration in variable scenarios. The model is based on YOLO, the improvements include three parts: a sample weighting strategy based on feature relevance, and a model-based parameter adaptive strategy and an optimal feature transformation adaptive migration strategy. Based on the above strategies, YOLO builds the similarity of the domain feature space, selectively migrates the source domain knowledge, and adjusts the boundary of the strategy to learn the invariant feature representation during the adaptation process to enhance the adaptive migration ability of the model. In the migration experiment, the migration ability of the three strategies is verified respectively, which effectively improves the stable detection of YOLO-based satellite components detection in the complex space environment. The experimental results show that the weight importance based on feature association learning is better than the random initial weight, the parameter adaptive transfer significantly improves the testing accuracy of the target domain, and the optimal feature transformation significantly improves the generalization ability of the model.