The reliability block diagram (RBD) is a graphical tool used for reliability modeling and analysis in various industries, including shipbuilding, aviation, and aerospace. Typically, RBDs are transformed into Bayesian networks for quantitative analysis of systems. Bayesian networks are probabilistic graphical models that can capture the uncertainties and causal relationships in complex systems. They can provide various reliability metrics such as failure probability, mean time to failure, availability, etc. However, these techniques have several drawbacks, especially for large-scale models, such as being extremely time and memory-consuming. To address these issues, we propose a hybrid method for quantitative analysis of large-scale RBDs based on the structure identification approach and binary decision diagrams. Theoretical analysis and case verification demonstrate that the proposed method is significantly more efficient than the current one.