Optimization of Rural Governance Resource Allocation Based on Data Mining: an Analysis Using Support Vector Machine Algorithm

With the implementation of the rural revitalization strategy, how to rationally allocate rural governance resources and improve the efficiency of resource utilization has become an urgent issue. By collecting and processing data on various rural governance resource allocations, this study employs the Support Vector Machine (SVM) algorithm for data modeling to explore the key influencing factors of resource allocation and optimize the existing resource distribution model. The results indicate that there are significant regional disparities in rural governance resource allocation, which are influenced by multiple factors such as economic development, infrastructure, and public service coverage. The SVM model successfully identified areas with imbalanced resource allocation, providing data support for the formulation of relevant policies. The study demonstrates that data mining technologies, especially the SVM algorithm, offer new ideas and methods for optimizing rural governance resource allocation, which has significant theoretical and practical value.