Abstract: Crown is one of the critical quality indicators for medium and heavy plate products, and its control level is a testament to an enterprise's production capacity and market competitiveness. With the vigorous development of artificial intelligence(AI) technology, intelligent control has become the core strategy driving the development of the iron and steel industry. However, there is still a lack of relevant research on how to apply AI technologies such as big data to improve product quality, especially in enhancing the plate crown control level during the medium and heavy plate rolling process. This study focuses on the plate crown control technology of medium and heavy plate mills. By combining mechanism models with data-driven models, an intelligent prediction model for heavy plate crown is established, achieving a hit rate of 83.04% for plate crown within the range of ±80 μm. Meanwhile, based on the theoretical framework of this model and big data analysis technology, the roll contour curve of the finishing mill work rolls is optimized. This optimization not only improves the crown control capability of medium and heavy plate mill products, but also extends the rolling cycle of work rolls, and increases both the mill productivity and product yield.