As the basic unit of residents’ activities and social management, communities are the disaster bearers of various public security emergencies. To improve the ability and level of community governance, as well as to strengthen the construction of resilient communities, a tripartite evolutionary game model of local government, developers, and home buyers is built, and numerical simulation is carried out using Matlab to analyze the impact mechanism of main parameters on the evolutionary stability strategy. The results show that: (1) The three parties’ different initial intentions will lead to different evolutionary stability strategies of the system, and the system’s final evolution result will reach the ideal state only when the initial willingness of developers and buyers is high. (2) The greater the government’s subsidy coefficient and the greater the regulatory intensity, the more likely it is that developers will choose to build resilient communities. (3) Public awareness of disaster prevention and mitigation is an important determinant of the purchase of resilient community housing strategies. To achieve rapid development of resilient communities, the intensity of regulation must be continuously improved, the public’s awareness of disaster prevention and mitigation must be strengthened, and the government’s regulatory costs must be reduced.

Evaluating the regional trends of air pollution disaster risk in areas of heavy industry and economically developed cities is vital for regional sustainable development. Until now, previous studies have mainly adopted a traditional weighted comprehensive evaluation method to analyze the air pollution disaster risk. This research has integrated principal component analysis (PCA), a genetic algorithm (GA) and a backpropagation (BP) neural network to evaluate the regional disaster risk. Hazard risk, hazard-laden environment sensitivity, hazard-bearing body vulnerability and disaster resilience were used to measure the degree of disaster risk. The main findings were: (1) the air pollution disaster risk index of Liaoning Province, Beijing, Shanghai and Guangdong Province increased year by year from 2010 to 2019; (2) the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) of each regional air pollution disaster risk index in 2019, as predicted by the PCA-GA-BP neural network, were 0.607, 0.317 and 20.3%, respectively; (3) the predicted results were more accurate than those using a PCA-BP neural network, GA-BP neural network, traditional BP neural network, support vector regression (SVR) or extreme gradient boosting (XGBoost), which verified that machine learning could be used as a method of air pollution disaster risk assessment to a considerable extent.