现代信息科技2026，Vol.10Issue(17)：7-12,6.DOI:10.19850/j.cnki.2096-4706.2026.17.002
MUD-SAPF:基于大模型的多源非结构化科研数据智能解析处理框架
MUD-SAPF:A Large-Model-Based Intelligent Parsing and Processing Framework for Multi-Source Unstructured Research Management Data
摘要
Abstract
Research management involves large volumes of unstructured data,such as credential documents,application guidelines,and project proposals,which traditional information systems struggle to effectively utilize due to heterogeneous layouts,complex semantics,and significant noise.This paper proposes MUD-SAPF,a multi-source unstructured data parsing and structuring framework for research management.By integrating deep layout analysis and multimodal semantic reasoning,the framework establishes an end-to-end data processing pipeline to efficiently structure and clean management data.Experimental results show that MUD-SAPF significantly reduces manual effort and improves recognition accuracy,processing efficiency,and the reliability of downstream analysis in typical application scenarios.The framework demonstrates strong engineering feasibility and supports the intelligent upgrading of research management systems.关键词
非结构化数据/多模态大模型/科研管理Key words
unstructured data/multimodal large models/research management分类
信息技术与安全科学引用本文复制引用
赵露曦,彭杰,谭琪,姬鹏,李论..MUD-SAPF:基于大模型的多源非结构化科研数据智能解析处理框架[J].现代信息科技,2026,10(17):7-12,6.基金项目
四川省科技计划项目(2024ZHYS0023) （2024ZHYS0023）
凉山州科技计划项目(25YYYJ0220) （25YYYJ0220）
