Abstract: Traditional static reliability assessment methods face challenges in characterizing the dynamic interaction and time dependency of digital control systems in nuclear power plants. To address this issue, this study proposes a dynamic reliability assessment method integrating dynamic fault tree(DFT) and dynamic Bayesian network(DBN) for digital main feedwater control system(DMFCS). First, DFT method was applied to establish a DFT model which characterized the dynamic interaction of DMFCS. Subsequently, the conversion strategy from DFT to DBN was implemented to construct a DBN model which characterized the time dependency of DMFCS. Finally, the DBN forward inference was conducted to obtain the reliability curve of DMFCS, while the DBN backward inference was conducted to calculate the posterior probabilities of root nodes, and the importance analysis was implemented to identify the vulnerable parts in the system. The results demonstrated that the proposed method can effectively characterize both dynamic interaction and time dependency of DMFCS, and the analytical outcomes provided scientific guidance for intelligent maintenance and routine inspections, thereby further enhancing the reliability and safety for DMFCS.