Development of sophisticated anomaly detection and diagnosis methods for spacecraft is one of the important problems in space system operation. In this study, we propose a diagnosis method for spacecraft using probabilistic reasoning and statistical learning with Dynamic Bayesian Networks (DBNs). In this method, the DBNs are initially created from prior knowledge, then modified or partly re-constructed by statistical learning from operation data, as a result adaptable and in-depth diagnosis is performed by probabilistic reasoning using the DBNs. This method fuses and uses both knowledge and data in a natural way and has the both ability which two polar approaches; knowledge-based and data-driven have. The proposed method was applied to the telemetry data that simulates malfunction of thrusters in rendezvous maneuver of spacecraft, and the effectiveness of the method was confirmed.