Data-Driven Operation Mechanism of Public Welfare Platforms for Left-Behind Children

Abstract

Left-behind children in rural China face persistent challenges related to psychological well-being, academic pressure, and insufficient social support. With the rapid development of digital technologies, public welfare platforms have become important tools to address these issues. However, most platforms still rely on experience-based management rather than systematic data utilization. This study is based on the practical implementation and field data of the “Yuguang Tongxing” program. This study proposes a data-driven operation mechanism for public welfare platforms serving left-behind children. By integrating insights from data-driven modeling, psychological measurement, stress heterogeneity, and public welfare governance, this paper constructs a mechanism that combines data integration, analysis, intelligent decision-making, and feedback optimization. Furthermore, testable hypotheses are proposed to enhance empirical applicability. The findings indicate that data-driven mechanisms can significantly improve service precision, resource allocation efficiency, and platform sustainability.