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【Objective】The intersection of big data and research on ‘three rural’ issues has created a research field with great potential. By combing the research results in this field, this paper has an in-depth understanding of its research focus and future direction, in order to provide some support for related research.【Method】 The paper used the methods of literature analysis and summary and induction to summarize and discussed the application of big data in the research of ‘three rural issues’ from three aspects: Research on agricultural issues, research on rural issues and research on farmers’ issues.【Result】With the popularization of communication infrastructure, big data such as internet usage data, communication sensor equipment data, geospatial data, organization management and operation data have been applied to the research of ‘three rural’ issues in China. At present, the application of big data in the research of ‘three rural’ in China has been constantly enriched. In the research of agricultural problems, big data has been applied to all links of agricultural production and operation, such as agricultural production monitoring and output improvement, and agricultural product market trend prediction. In the research on rural issues, the application of big data mainly focused on depicting the current situation of rural development, especially the evaluation of rural spatial environment, which can find a large number of rural spatial structures and systems driven by actual social and economic activities. In the research on farmers’ problems, the problem areas applied by big data mainly include the identification of farmers’ spatio-temporal behavior laws and the wealth and well-being of farmers reflected by them. High coverage data reflecting individual behavior, such as mobile phone use and signaling positioning data, has been the main data sources for such research.【Conclusion】The application of big data in the research of ‘three rural’ issues in China shows great development prospects in the development of agricultural industry, farmers’ lives and spatial planning, but there are also challenges in the aspects of data source and quality, method design, integration of traditional governance experience, etc. [ABSTRACT FROM AUTHOR]