大语言模型在乡村全面振兴评价中的应用

Application of Large Language Models in the Evaluation of Comprehensive Rural Revitalization

摘要: 传统乡村振兴评价方法存在指标单一、数据更新滞后及维度失衡等局限，本研究提出基于大语言模型(LLM)的测度新方法。通过融合结构化统计数据与非结构化文本(政策、舆情)，构建包含产业兴旺、生态宜居、乡风文明、治理有效、生活富裕五个维度涉及30个指标的评价体系；采用多Agent架构协同DeepSeek等大模型，结合基于情感分析的权重机制与冲突检测算法，实现精准评估。基于河北省2014~2023年数据的实证表明，十年间乡村振兴综合得分提升76.2%，五维度均显著增长，但区域差异明显。大语言模型驱动的多源融合测度框架有效克服传统评价方法的缺陷，为乡村振兴动态监测与精准施策提供科学工具。

Abstract: Traditional rural revitalization evaluation methods face limitations such as singular indicators, delayed data updates, and dimensional imbalances. This study proposes a novel measurement approach leveraging large language models (LLMs). By integrating structured statistical data with unstructured text (policies, public sentiment), an evaluation system encompassing five dimensions—thriving businesses, pleasant living environments, social etiquette and civility, effective governance, and prosperity—with 30 specific indicators was constructed. Utilizing a multi-agent architecture coordinated with LLMs like DeepSeek, and incorporating an emotion analysis-based weighting mechanism and conflict detection algorithm, precise assessment was achieved. Empirical analysis based on data from Hebei Province from 2014 to 2023 shows a 76.2% increase in the comprehensive rural revitalization score over the decade, with significant growth across all five dimensions, though regional disparities remain evident. The LLM-driven multi-source fusion measurement framework effectively overcomes the shortcomings of traditional evaluation methods, providing a scientific tool for dynamic monitoring and targeted interventions in rural revitalization.