<p>As China’s industrial strength continues to develop, a growing number of challenges have emerged in the field of industrial management. This study proposes an industrial economic management decision model based on three parameter interval grey numbers to address the issues of high information uncertainty and complexity in industrial economic management decisions. This model characterizes the uncertainty of decision information by introducing three parameter interval grey numbers, and innovatively adopts grey relational clustering decision-making method, combined with genetic algorithm to dynamically optimize key parameters. The results demonstrated that the model exhibited excellent performance on multiple datasets, with an accuracy stable between 95 and 96%. In the application of industrial economic management datasets, the model increased the average profit margin of enterprises by 5.3% and reduced unit costs by an average of 13.3%, which was significantly better than other comparative models. In addition, through comparative experiments with other decision models, this model has shown superiority in improving profit margins, increasing sales, reducing unit costs, and reducing prediction errors. Therefore, the proposed industrial management decision model can effectively handle uncertainty factors, enhance the scientific and effective nature of industrial economic management decisions, and provide more accurate and reliable solutions for industrial enterprises.</p>