Leveraging machine learning in a comparative analysis of rural revitalization policies in China and global best practices

This study conducts a comparative analysis of rural revitalization policies in China and five developed nations (the United States, United Kingdom, Japan, Germany, and South Korea) using a machine learning-driven framework. By examining 71 policy documents, the research categorizes policies into four domains—Market Capital, Balanced Coordination, Government Regulation, and Rural Ethics—and identifies distinct policy typologies. The findings reveal that China predominantly employs “Government Regulation Dominant” strategies, reflecting its centralized governance model, while wealthier nations favour integrated and market-oriented approaches. A significant correlation is observed between GDP per capita and policy orientation, with higher-income countries emphasizing market-driven mechanisms and lower-income nations relying more on state intervention. The study also highlights temporal shifts in policy preferences, illustrating how historical and socio-economic contexts shape rural development strategies. Based on global best practices, the paper proposes actionable recommendations for China, including integrating market capital with regulatory oversight, diversifying policy frameworks, and fostering public-private partnerships. The research underscores the importance of adapting policies to regional characteristics and leveraging computational methods for robust policy analysis. Limitations include the focus on high-income and upper-middle-income countries, suggesting the need for future studies to explore frameworks applicable to developing regions. This work contributes to the discourse on sustainable rural development by offering empirical insights and a scalable analytical methodology.