This paper presents a novel prediction model for Regional Economic Development (RED) levels using the Self-Organizing Map (SOM) algorithm. It posits that the SOM algorithm can effectively forecast RED levels by processing multidimensional datasets that include economic, social, and environmental indicators. The approach involves constructing an Evaluation Index System (EIS) comprising 14 indicators across three domains: financial performance, social conditions, and ecological sustainability. The performance of the SOM model is compared to the traditional Support Vector Regression (SVR) model. The analytical tools utilized in this study include the SOM neural network and the SVR model, both applied to predict the Gross Domestic Product (GDP) and the Consumer Price Index (CPI) in Region C. The SOM model achieves a relative error of 0.01% in predicting GDP and 0.12% in predicting CPI, outperforming the SVR model. Additionally, the model is applied to forecast the economic development levels of two Chinese provinces from 2026 to 2035, revealing significant regional disparities. The findings suggest that the SOM algorithm is a promising tool for predicting RED, providing valuable insights for policy-making processes.
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