Research on AI-Driven Economic Data Intelligent Mining Algorithms

This study focuses on artificial intelligence-driven economic data mining algorithms, exploring core algorithms such as data preprocessing, feature engineering, regression, classification, clustering, and time series analysis. Through rigorous experimental design and performance evaluation, the research finds that Elastic Net excels in GDP forecasting, XGBoost shows significant effectiveness in credit assessment tasks, Gaussian Mixture Model demonstrates clear advantages in customer segmentation, and the Prophet-LSTM hybrid model performs outstandingly in CPI forecasting. These algorithms surpass traditional methods, exhibiting strong robustness and adaptability, providing powerful tools for economic data analysis, enhancing the accuracy of economic forecasting and decision-making, and holding significant implications for the development of the digital economy.