The Satisfaction Improvement Strategy of Green Development Based on the AHP-entropy Weight Method and CART Algorithm

In this study, the AHP-entropy weight method is used to weigh various indicators that affect green development, establish urban green development evaluation indicators, and then explore the important factors that affect people's satisfaction with green development through the Classification Regression Tree algorithm. Finally, a prediction model is established. It was found that the classification and regression model prediction results of the CART algorithm have high accuracy. The city's green coverage area indicator is the most important indicator through the AHP-entropy weight method. By using the CART algorithm, the most important factors affecting citizens' satisfaction with the green development of Xiamen City are the actions of the municipal government. Based on the research results, an improvement strategy for green development satisfaction in Xiamen is proposed.