Research on economic system modeling based on artificial intelligence algorithms

This study explores the methods of economic system modeling and analysis based on artificial intelligence algorithms. By preprocessing and performing feature engineering on China's quarterly GDP data from 2010 to 2023, we constructed an ensemble learning model combining LSTM, Random Forest, and XGBoost. We introduced an attention mechanism and dynamic loss function to optimize model performance. Experimental results indicate that the model performs excellently across different economic periods, particularly showing the highest accuracy during stable growth phases. The model demonstrates good robustness to slight data disturbances but exhibits a decline in performance under significant noise or high missing rates. Comprehensive evaluation reveals that the model has an RMSE of 213.7 billion yuan, MAPE of 2.3%, and directional accuracy of 89.3%, outperforming traditional time series models. The research findings provide robust technical support for economic forecasting and decision-making.