Regional economic development not only impacts regional politicization and economic construction but also improves national comprehensive competitiveness.How to predict the relationship between regional stability and economic development is an important problem.Accurately and quantitatively explaining development trends by using historical regional economic development data to analyze future development of the region can be difficult owing to the complexity of economic development.The goal of this work is to explore the dynamic,complex,and interactive process of the conflict caused by environmental change and analyze the potential correlation between environmental change and economic growth by regarding regional stability as a measure of the relationship between environmental change and economic growth.The main aspects of this work are as follows:1)Using the Fitness and Complexity algorithm achieves better performance in predicting national GDP growth.By applying machine learning models,we can predict stability categories of different countries with a prediction accuracy of 90%.2)We perform a correlation visualization analysis of data-based environmental change and regional economic growth.We find that some developing countries have strong economic stability associated with water resources and carbon dioxide emissions,whereas the economic stability of developed countries is associated with per capita arable land.3)We propose new indicators.Compared with the current mainstream indicators,the new indicators are more focused on quantification of raw data,reducing the impact of conceptual abstraction indicators on forecast performance,which makes them more responsive to assessing a country's stabilization.Through actual forecasting effect analysis,the new ranking compensates for the prediction distortion defects caused by the abstract indicators in the world mainstream ranking when measuring regional stability and can satisfy the basic regional stability prediction function.Moreover,it may help us to better understand the prediction results with factor explanation.