A tunnel is a complex network system with multiple risk factors interacting. At present, the cause analysis of tunnel fire accidents focuses on exploring risk sources and risk assessment, ignoring the interaction between risk factors. A single model has certain limitations. By proposing the concept of the multi-factor coupled evolutionary game of tunnel fire, integrating the natural killing model (NK) and the explanatory structure model (ISM), the evolutionary game of multi-factor coupling of tunnel fire is studied from the perspective of micro and macro analysis, qualitative and quantitative research, the coupling relationship and effect between risk factors are discussed, 100 tunnel fire accidents and 158 tunnel fire literature at home and abroad are analyzed, and 40 typical tunnel fire risk factors and 31 coupling types of fire cause factors are extracted. Using the combined ISM-NK model, a seven-level network model of tunnel fire accident risk coupling is constructed, and the degree of coupling of various types of risk factors is evaluated. The hierarchical network cascade model revealed that 4 of the 40 typical tunnel fire risk factors were the underlying risk factors, 23 shallow layers were the risk factors and direct influencing factors, and 13 were the middle-risk factors and indirect influencing factors. The NK model shows that with the increase of coupling nodes, the frequency of tunnel fire accidents also shows an upward trend, and the subjective risk factor coupled with tunnel fires have a higher frequency than the objective risk factors.

In order to effectively reduce the probability of subway operation accidents and explore the key risk factors and multi-factor risk coupling mechanism during the subway operation period, this paper classifies the risk factors affecting subway operation safety into four categories of primary risk factors, personnel, equipment and facilities, environment and safety management, introduces the emergency management concept to identify 18 secondary risk factors, combines the improved fuzzy decision making test and evaluation laboratory (DEMATEL) and Explanatory Structure Model (ISM) to visualize the risk factor action relationship, construct a six-order hierarchical recursive structure model for subway operation accidents, explore the coupling relationship and effect between risk factors from the perspective of single factor, double factor and multiple factors, establish a coupling effect metric model based on Natural Killing Model (N-K), carry out coupling information interaction scenario combination and coupling effect quantification calculation, and finally integrate fuzzy DEMATEL-ISM-NK model to correct the centrality, determine the key risk factors in subway operation accidents from the perspective of macro and micro analysis, qualitative and quantitative research, and propose safety prevention and control strategies accordingly. The results show that six factors, such as emergency management and social environment, are key risk factors to be prevented in the metro operation system. Multi-factor risk coupling leads to a higher probability of subway operation accidents, and controlling multi-factor involvement in coupling is an effective means to reduce the occurrence of subway operation accidents.

To effectively prevent the occurrence of poisoning and asphyxiation accidents in underground mines, this paper establishes an evaluation index system for the factors influencing accidents, constructs a combined assignment model to solve the problem of low accuracy of assignment results caused by a single algorithm, predicts the CO concentration after blasting because CO poisoning is the main cause of accidents, explores the accuracy of different time series prediction methods, and projects the required ventilation after blasting to ensure the safe operation of personnel. Firstly, starting from “man-machine-environment-management”, social factors are introduced to build an evaluation index system. Secondly, three combinatorial allocation models were compared, namely rough set theory–G1 method (RS-G1), entropy method–G1 method (Entropy-G1), and CRITIC method–G1 method (CRITIC-G1). The best model was selected and the allocation rating model was constructed in combination with the cloud model, and the mine risk level was evaluated by using the model. Thirdly, the GM(1,1) model, the quadratic exponential smoothing method, and the ARIMA model were compared by calculating posterior differences and errors, and the method with the highest accuracy was selected for predicting CO concentration. The results show that the inclusion of social assessment indexes in the assessment index system makes the consideration of assessment indexes more comprehensive. The RS-G1 combined assignment model achieved higher accuracy than other combined assignment models, and the GM(1,1) model had the highest accuracy and the best prediction effect. The results of the study can help provide targeted prevention and management measures for poisoning and asphyxiation accidents in underground mines.