抄録
With the increasing significance of microorganisms in agriculture, environmental protection, and biotechnology, accurately deciphering the functions of individual strains has become a core issue in both fundamental and applied research. Traditional experimental and statistical approaches face limitations in terms of data scale, complexity, and accuracy. In contrast, artificial intelligence (AI) technologies—including intense learning and machine learning—have substantially improved the precision and efficiency of function prediction by effectively analyzing genomic information and integrating multi-omics data, such as transcriptomics and metabolomics. This study systematically outlines the AI-based technological framework for strain function prediction, detailing key methods and workflows in genomic data analysis, functional annotation, and the integration of multi-omics data. Taking agricultural applications such as salt-alkali-tolerant crops as examples, it further explores the potential advantages of AI-driven strain selection in accelerating crop improvement. The study also provides an in-depth analysis of current technical challenges, including data quality control, model generalization, and coordinated processing of multi-omics data, and proposes future research directions and improvement strategies. This study aims to offer theoretical support and practical guidance for advancing microbial function prediction technologies and promoting the development of intelligent and precision agriculture.
