基于LDA模型的中国智慧农业政策文本量化分析

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中图分类号：F320 文献标志码：A 文章编号：1004-390X（2025）06-0076-08

Abstract： In the context agricultural modernization， accelerating the development smart agriculture as a key task holds significant importance for securing a leading position in the global agricultural lscape. This paper adopted a policy text analysis perspective to systematically trace the evolution China's smart agriculture policies， identify core themes underlying patterns， reveal current policy hotspots， trends， existing shortcomings， subsequently propose policy recommendations to improve the relevant policy framework. The study found that， policy evolution exhibited a "central government-led，local government-followed” characteristic， with smart agriculture policies covering a broad range themes， forming a clear-layered， systematic policy framework. In recent years，smart agriculture had achieved significant results in promoting industrial integration upgrading， improving agricultural production eficiency， supporting rural revitalization， with vast development potential. However， its development faced multi-dimensional challenges such as technological bottlenecks， talent shortages， data governance， necessitating the dynamic optimization precise adaptation the policy system to provide effective support for the high-quality development smart agriculture. To this end， policy optimization recommendations are proposed from four aspects： strengthening central-local coordination mechanisms， establishing a diversified talent cultivation system， deepening the integration smart agriculture digital rural governance， intensifying core technology research application. These recommendations aim to provide theoretical foundations practical pathways for improving China's smart agriculture policy framework.

Keywords： smart agriculture; policy text; Latent Dirichlet Allocation model; text mining; social network analysis

习近平总书记高度重视现代信息技术在农业中的应用和发展，指出“瞄准农业现代化主攻方向，提高农业生产智能化、经营网络化水平，帮助广大农民增加收入”。（剩余11757字）