The resilience theory has been introduced into the aviation logistics supply chain to enhance its ability to withstand internal and external shocks and uncertainty. A resilience evaluation model for the aviation logistics supply chain based on a Bayesian network was proposed. The Bayesian network structure index was established from the macro performance layer, the middle influence layer, and the bottom cause layer. Subsequently, the Bayesian network model was constructed, and the probability inference process of the Bayesian network nodes was completed using the Markov Chain Monte Carlo algorithm. This enabled the acquisition of the probability distribution of aviation logistics supply chain resilience through parameter learning and model inference. Furthermore, the Bayesian network model was employed for reverse reasoning, sensitivity analysis, and cause chain analysis to identify the key variables and sensitive factors affecting the resilience of the aviation supply chain. Based on these analyses, suggestions for improving resilience were put forward. Research findings indicate that the resilience value of China's aviation logistics supply chain is approximately 53%, signifying a high level of resilience. The Bayesian network model has proven effective in quantifying the resilience value of the aviation logistics supply chain, as well as in reasoning and analyzing the influence of each factor on resilience value, thus contributing to the enhancement of the risk resistance ability of the aviation logistics supply chain.