Exploring the Application of Artificial Intelligence Technology in University Archives Management
Abstract
As core information resources documenting the entire process of teaching, research, and administration, university archives directly
impact institutional governance efficiency and the preservation of historical continuity. With the expansion of higher education and accelerated digital transformation, traditional "manual + digital" archival management models face challenges including inefficient classification,
time-consuming retrieval, high security risks, and underutilized value. This paper systematically analyzes the applicability of artificial intelligence in university archives management, considering their characteristics of "diverse types, massive volume, and multi-scenario utilization."
Through four key application scenariosintelligent collection, automated processing, precision services, and security preventionthe study
explores practical implementation pathways. It proposes optimization strategies addressing technical implementation issues such as inconsistent data standards, talent shortages, and ethical risks, providing actionable references for advancing university archival management from
"digitization" to "smart transformation."
impact institutional governance efficiency and the preservation of historical continuity. With the expansion of higher education and accelerated digital transformation, traditional "manual + digital" archival management models face challenges including inefficient classification,
time-consuming retrieval, high security risks, and underutilized value. This paper systematically analyzes the applicability of artificial intelligence in university archives management, considering their characteristics of "diverse types, massive volume, and multi-scenario utilization."
Through four key application scenariosintelligent collection, automated processing, precision services, and security preventionthe study
explores practical implementation pathways. It proposes optimization strategies addressing technical implementation issues such as inconsistent data standards, talent shortages, and ethical risks, providing actionable references for advancing university archival management from
"digitization" to "smart transformation."
Keywords
Artificial intelligence; University archives management; Smart archives; Natural language processing; Machine learning
Full Text:
PDFReferences
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DOI: http://dx.doi.org/10.70711/neet.v3i11.8094
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