Research on Optimizing Economic Decision Support System Using Data Mining Technology

This paper studies how to use data mining technology to optimize economic decision support system. Traditional economic decision models often rely on static rules and assumptions, which are difficult to cope with the ever-changing economic environment. This study proposes a data mining optimization algorithm based on multi-model forecast fusion, which combines regression analysis, SVM and neural network, and improves the adaptability and accuracy of the model through a hierarchical structure. Experimental results show that the proposed model is significantly better than the traditional model in multiple economic forecasting tasks, especially when the economic fluctuations are large, it can provide more accurate decision support. In the specific experiment, historical economic data was used for training and verification, and the mean square error (MSE) of the model was reduced by more than 15% compared with the traditional model, and the real-time and accuracy of the decision were greatly improved. Through comparative experiments, this paper proves the effectiveness of the multi-model fusion strategy in economic decision-making and demonstrates its great potential in practical applications.