Artificial Intelligence in Functional Polysaccharides for Food Applications: Process Optimization, Structure–Function Decoding, and Rational Design

Functional polysaccharides are widely used as food ingredients but are hindered by extreme structural heterogeneity, poorly defined structure-function relationships, and inefficient trial-and-error production workflows. This review provides an integrative synthesis of how AI is reshaping functional polysaccharide research toward food-grade ingredients and formulations. We organize recent advances into a three-stage framework: (1) efficiency amplification, where machine-learning models improve extraction/fermentation optimization and enable rapid analysis when coupled with spectroscopic fingerprints; (2) mechanism-informed hypothesis generation, where deep Deep-QSAR, graph-based learning, and interpretable modeling begin to uncover quantitative links between structural motifs and functional properties, including microbiome-mediated effects relevant to health; and (3) design assistance, in which AI supports precision-guided polysaccharide engineering and formulation for targeted food functionalities. By bridging computational advances with experimental validation, this review provides a cohesive roadmap for polysaccharide discovery and discusses key translational barriers─data scarcity and standardization, model generalizability and interpretability, and regulatory acceptance─highlighting practical strategies for AI-guided polysaccharide discovery and application.