The increasing number of space debris and space-active targets makes the space environment more and more complex. Space target recognition, a crucial component of space situational awareness, is of paramount importance to space security. Firstly, this paper elucidates the fundamental principles of ensemble learning, analyzes its characteristics and fusion method, and provides a comprehensive comparison of three common ensemble learning methods. Secondly, this paper analyzes the basic attributes and characteristics of space targets and categorizes the hierarchy of space target recognition. Again, the paper reviews the advances in the application of ensemble learning in space target recognition, focusing on three aspects: space target recognition datasets, the ensemble of traditional machine learning models, and ensemble deep learning. Subsequently, classical machine learning and ensemble learning algorithms are tested on a self-built space target simulation dataset, and we find that Stacking performs well on this dataset. Finally, the paper discusses future research directions.