TITLE:
Building a Digital Profiling Information Platform for Primary-Level Party Organizations and Party Members in Higher Education Institutions: A Case Study of China University of Geosciences (Beijing)
AUTHORS:
Man Liu
KEYWORDS:
Smart Party Building in Higher Education, Party Building Digitalization, Digital Profiling of Party Organizations, Digital Profiling of Party Members
JOURNAL NAME:
Creative Education,
Vol.17 No.10,
October
13,
2026
ABSTRACT: In the new era, the digitalization strategy in education is driving primary-level Party building in higher education institutions to transition from traditional ledger-based management to precision governance through big data. Digital profiling of primary-level Party organizations and Party members, as a core application of smart Party building, can address practical challenges such as subjective evaluation, coarse governance, and fragmented data. Taking the current status of Party building informatization at China University of Geosciences (Beijing) as a sample, the university faces bottlenecks including the independent operation of multiple Party-building systems across departments, insufficient data interconnection, a complete absence of advanced AI applications, and weak decision-support capabilities. This paper reviews the current construction practices of digital profiling systems for Party organizations and Party members in domestic universities, constructs an indicator system for organizational and individual Party member profiles tailored to the institutional characteristics of CUGB, clarifies the full-domain data sources and data flow logic, and conducts an in-depth analysis of core challenges in platform construction, including data silos, indicator adaptability, security and compliance, and technical talent. It also provides practical references for the implementation of digital profiling platforms in similarly industry-featured higher education institutions, facilitating the transformation of primary-level Party building from “experience-based judgment” to “data-empowered decision-making”.