<p>The deepwater subsea wellhead (SW) system is the foundation for the construction of oil and gas wells and the crucial channel for operation. During riser connection operation, the SW system is subjected to cyclic dynamic loads which cause fatigue damage to the SW system, and continuously accumulated fatigue damage leads to fatigue failure of the SW system, rupture, and even blowout accidents. This paper proposes a hybrid Bayesian network (HBN)-based dynamic reliability assessment approach for deepwater SW systems during their service life. In the proposed approach, the relationship between the accumulation of fatigue damage and the fatigue failure probability of the SW system is predicted, only considering normal conditions. The HBN model, which includes the accumulation of fatigue damage under normal conditions and the other factors affecting the fatigue of the SW system, is subsequently developed. When predictive and diagnostic analysis techniques are adopted, the dynamic reliability of the SW system is achieved, and the most influential factors are determined. Finally, corresponding safety control measures are proposed to improve the reliability of the SW system effectively. The results illustrate that the fatigue failure speed increases rapidly when the accumulation fatigue damage is larger than 0.45 under normal conditions and that the reliability of the SW system is larger than 94% within the design life.</p>