用大模型解析汉代文献，多维度呈现思想与历史的互动
HistoLens: An LLM-Powered Framework for Multi-Layered Analysis of Historical Texts -- A Case Application of Yantie Lun
- 基于大模型构建文本分析框架，融合实体识别与知识图谱
- 揭示儒法思想对西汉政经军民的影响，量化对比其作用
- 支持可解释教学场景，适合历史研究与教育应用
本文提出HistoLens，一种基于大语言模型（LLMs）的多层级历史文本分析框架。以西汉重要典籍《盐铁论》为案例，展示该框架在历史研究与教育中的潜力。HistoLens整合自然语言处理技术，包括命名实体识别、知识图谱构建与地理信息可视化。通过多维、可视、定量方法，深入探讨《盐铁论》中儒家与法家思想对政治、经济、军事及民族关系的影响。同时，基于大模型辅助提取的儒法思想数据集，构建可解释的机器教学场景。该方法为《盐铁论》等历史文献研究提供新视角，并为历史教育提供新型辅助工具。框架旨在赋能历史学家与学习者，实现对历史文本的深度、多层次分析，推动历史教育创新。
原文摘要 · Abstract (English)
This paper proposes HistoLens, a multi-layered analysis framework for historical texts based on Large Language Models (LLMs). Using the important Western Han dynasty text "Yantie Lun" as a case study, we demonstrate the framework's potential applications in historical research and education. HistoLens integrates NLP technology (especially LLMs), including named entity recognition, knowledge graph construction, and geographic information visualization. The paper showcases how HistoLens explores Western Han culture in "Yantie Lun" through multi-dimensional, visual, and quantitative methods, focusing particularly on the influence of Confucian and Legalist thoughts on political, economic, military, and ethnic. We also demonstrate how HistoLens constructs a machine teaching scenario using LLMs for explainable analysis, based on a dataset of Confucian and Legalist ideas extracted with LLM assistance. This approach offers novel and diverse perspectives for studying historical texts like "Yantie Lun" and provides new auxiliary tools for history education. The framework aims to equip historians and learners with LLM-assisted tools to facilitate in-depth, multi-layered analysis of historical texts and foster innovation in historical education.
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