## 1 Introduction
For the reason that current safety risk analysis technology is insufficient to solve the coupling problem among risk factors, scholars from various fields have implemented valuable explorations on qualitative coupling mechanism analysis, and applied them to the field of safety risk analysis. For example, [PERSON] [17] proposed a hierarchical and structured safety assessment method, which solved the complex relationship among many risk factors in air transportation, [PERSON] et al. [18] studied the coupling mechanism of safety risk in air traffic control and put forward a strategy of safety risk coupling management, [PERSON] et al. [19] analyzed the original causes of aviation accidents and constructed a model of an aircraft safety management diagram based on coupling theory, and [PERSON] et al. [20; 21] studied the coupling mechanism between failures and proposed a phased-mission system analysis method considering common cause failures, using XML to realize automatic modeling and analysis. Moreover, many scholars use visual models and coupling coordination theory to establish multi-factor coupling mechanism models, which are widely used in the fields of coal mine production [22], waterway transportation, and highway transportation [23]. However, the above safety analysis methods mainly focused on a qualitative description and the study of coupling mechanisms, and lack the quantitative analyses and evaluations of coupling relationships. One way to evaluate a coupling relationship is to adopt a reasonable mathematical model to assess the degree of mutual influence between factors, and there has been various models developed, such as structural equation modeling (SEM), interpretative structural modeling (ISM), N-K models, etc. Numerous studies have demonstrated that these methods are powerful and efficient. For example, [PERSON] et al. [24] adopted the SEM model to analyze the causal relationship between risk factors in the coal mine safety, and the results provided guidance for practical risk assessment. [PERSON] et al. [25] utilized the SEM model to discuss the relationship between various variables of business performance of an insurance company. [PERSON] et al. [26] and [PERSON] et al. [27] discovered the most direct and fundamental factors through application of an ISM model. [PERSON] et al. [28] calculated the probability of multi-factor coupling by using an N-K model. However, the application of the above methods requires a large amount of historical and archived data, which may result in a limitation on the use of the methods in view of the potential lack of integrity and sample size of the data. On the other hand, the Decision Making Trial and Evaluation Laboratory (DEMATEL) model, developed by the Battelle Memorial Institute [29], is a key technique in resolving problems associated with the coupling relationship. It was initially applied to investigate complex world problems, including racial issues, starvation, environmental protection, and energy consumption [30; 31]. The DEMATEL model can transform sophisticated systems into precise causal relationships in structure, so that the quantified extent of direct and/or indirect causality among coupled risk factors can be evaluated using matrix operations and mathematical theories to help find the core problem [32; 33]. This paper presents a combined technology based on the DEMATEL model and a Bayesian network, aiming at analyzing and evaluating the risk of factors considering the coupling relationship, so as to identify the key risk factors and high-risk regions. There are two benefits to using the proposed technology. Firstly, the structure diagram of the BN is established based on the direct-relation matrix; secondly, the conditional probability is calculated based on the direct-indirect matrix, and the parameterization of the Bayesian network is then completed.