The introduction of machine learning algorithms provides a new perspective and tool for the accessibility analysis of rural digital financial services. By optimizing resource allocation, improving service efficiency, and enhancing user satisfaction, machine learning can significantly improve the coverage and quality of financial services in rural areas. However, in current research and practice, the application of machine learning algorithms in rural digital financial services has not received sufficient attention, resulting in low resource utilization and insufficient service efficiency. This paper applies Linear Discriminant Analysis (LDA) in machine learning algorithms to the field of rural digital financial services and analyzes its support for the development of the real economy. Through testing rural financial institutions in different regions, it is found that the digital financial service model optimized by machine learning algorithms can significantly improve resource utilization, promote the sustainable development of the financial ecology, and effectively improve user satisfaction scores. The experimental results show that the application of machine learning algorithms has increased user satisfaction by 6.6% and significantly improved the ecological and environmental protection index and efficiency of financial services.
