Abstract: To address the issues of overly conservative and insufficiently quantified safety factor selection grounded in traditional experience in aerospace structural design, this paper proposes a safety factor quantification method based on Bayesian credible reliability. Building on the analytical framework that relates safety factors to reliability in probabilistic models, the mapping between safety factors and reliability is derived for non-probabilistic models. Structural stress and strength are modeled credibly according to Bayesian principles and employed to compute non-probabilistic credible reliability. The uncertainty model parameters are dynamically updated utilizing actual sample data to subsequently calibrate safety factors, establishing a refined dynamic quantification method for safety factors. Numerical case studies of a representative rod and a wing structure validate the effectiveness of the proposed method, provide safety factor values under different credibility levels, and discuss the influence of credible reliability indices and sample size on safety factor selection in structural design. The numerical results demonstrate that the proposed method can effectively mitigate the conservatism of traditional experience-based safety factors, offering new foundations and guidance for the lightweight design of advanced structures in the aerospace field.