Aiming at the uncertainty of the orbit status of non-cooperative space objects, a dynamic inference method for the orbit status of space objects based on dynamic Bayesian networks is proposed. First, the semantic model for the orbit status of space objects is established, and the semantic relationships such as the orbit status, orbit class and orbit change are explained. Second, the orbit status characteristics are analyzed, and the hierarchical division method for coplanar and noncoplanar orbit change is constructed. Then, based on the dynamic Bayesian network, an inference method for the orbit status of space objects is established, and the relationships between orbit status, orbit class, and orbit change are used to obtain the dynamic change process of the orbit status. Finally, the proposed method is validated by comparing with actual situations of space objects of different orbital classes. Experimental results demonstrate that the proposed dynamic inference method for the orbit status of space objects can inference the orbit status with uncertainty and obtain the change process, which provides support and assistance for further decision-making.