In order to evaluate the visual fatigue caused by 3D equipment observation more effectively, the dynamic Bayesian network is used to calculate the stereoscopic visual fatigue probability of 3D observers for the first time. In the process of constructing a directed acyclic graph, the relationship between multiple factors in stereo vision and fatigue phenomena is taken into accountthe physiological characteristic nodes and dynamic factors are added for making reasonable estimation. The state of each node is in one-to-one corres- pondence with the state probability of Bayesian network nodes, which provides a systematic scheme for stereoscopic fatigue probability assessment. The results show that: Compared with current MOS method, the scheme of dynamic Bayesian network analyzes the observation more comprehensively, the fatigue state of the observer is more accurate than the subjective result of the observer himself, and is closer to the actual situation.