In recent years, the rapid development of big data technology has brought profound changes to agriculture, especially in the research of rice disease resistance genes. This paper systematically reviews the application of big data technology in the mining and functional research of rice disease resistance genes. First, the basic concepts of big data technology and its current applications in agriculture are outlined. Then, the limitations of traditional research methods for studying major rice diseases and their resistance genes are analyzed. Subsequently, the critical roles of big data technology in key aspects of rice disease resistance gene research are discussed in detail, including data collection, cleaning, integration, standardization, and feature extraction. Moreover, the primary methods for gene mining are introduced, such as gene association analysis, genome-wide association studies, machine learning, and deep learning techniques. The paper further explores the techniques for functional prediction and validation of resistance genes, as well as methods for studying their molecular mechanisms. Through typical case studies, the specific applications of big data technology in researching resistance genes for major rice diseases, such as rice blasts and rice false smut, are demonstrated. Finally, the paper summarizes the challenges and future development directions of big data technology in rice disease resistance gene research. It emphasizes issues like data quality control, data sharing, and the complexity of gene functional studies, while envisioning the broad prospects of big data technology in future agricultural research. This paper provides valuable academic references for advancing research on rice disease resistance genes.