A Review of Artificial Intelligence Applications in Baijiu Research: From Experience to Data

Baijiu, a traditional Chinese distilled spirit with profound cultural and economic significance, faces long-standing challenges in standardization, quality consistency, and skill inheritance due to its empirical production model. The rapid advancement of artificial intelligence (AI) and multi-omics technologies is driving a paradigm shift in Baijiu research from experience-driven to data-driven approaches. This review systematically summarizes the current state of AI applications across the entire Baijiu industry chain. Common AI methods including traditional machine learning, deep learning, multimodal data fusion, and emerging paradigms such as explainable AI (XAI), genome-scale metabolic models (GEMs), and few-shot learning are critically examined. Key bottlenecks—data silos, small sample sizes, model interpretability, and the tension between technology and tradition—are discussed in depth. Future directions are proposed, including multimodal fusion, digital twins, hybrid mechanistic–data modeling, closed-loop control, human–machine collaboration, standardization, and ethical governance. This review provides a comprehensive framework for integrating AI into Baijiu research and offers references for intelligent transformation in other fermented food systems.