Amidst the rapid evolution of the digital economy, this research positions fuzzy rough set theory at the forefront of industrial economic modeling, acknowledging its profound capability to manage uncertain information. This theory’s analytical precision facilitates a deep dive into the dynamics of industrial economic growth, enabling the refinement of industrial structures for sustainable development. Our empirical investigation into region A’s grain industry showcases a remarkable output growth of 272.88% from 2008 to 2018, with a model accuracy exceeding 95%. Analysis on cost inputs further demonstrates how innovation in science and technology plays a crucial role in cost reduction. By leveraging fuzzy rough set theory, this work contributes significantly to the strategic development of industrial economics, offering a robust methodology for data-driven analysis and forecasting in industrial sectors.