Research on Bottleneck Identification and Decision Optimization of Public Welfare Development from the Perspective of Multi-Source Data Fusion

DOI: https://doi.org/10.62517/jnme.202510507

Author(s)

Han Wang, Jing Zhang

Affiliation(s)

Liaoning Institute of Science and Technology, Benxi, Liaoning, China

Abstract

From the core perspective of multi-source data fusion technology, this paper addresses the practical pain points of public welfare undertakings, such as "data fragmentation, experience-based decision-making, and difficulty in quantifying effectiveness". Through literature analysis, case disassembly, and model construction, it identifies the core bottlenecks in data application of current public welfare undertakings, and further proposes a decision optimization path based on multi-source data fusion. The research finds that data barriers, insufficient technical adaptation, and weak value transformation capabilities are the key issues restricting the high-quality development of public welfare undertakings. The "data layer - feature layer - decision layer" three-level fusion model can effectively improve the accuracy, transparency, and sustainability of public welfare projects, providing theoretical references and practical paradigms for the digital transformation of public welfare undertakings.

Keywords

Multi-Source Data Fusion; Public Welfare Undertakings; Bottleneck Identification; Decision Optimization; Digital Transformation

References

[1]Wang Ming, Li Yong. China Charity Development Report (2024) [M]. Beijing: Social Sciences Academic Press (China), 2024. [2]Zhang Li, Liu Jun. Research on the Application of Multi-Source Data Fusion Technology in the Field of Public Services [J]. Journal of Intelligence, 2023, 42(5): 123-129. [3]Ministry of Civil Affairs. Measures for the Disclosure of Charitable Organization Information (Revised Version) [Z]. 2022. [4]Tencent Public Welfare. 2023 Public Welfare Data Fusion Practice Report [R]. Shenzhen: Tencent Public Welfare Foundation, 2024.