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Scientific Information Research

Keywords

digital humanities; Large language model; GLM-4; promt engneering; Chain of Thought; machine learning

Abstract

[Purpose/significance]This paper aims to explore the evolution trend of research methods in the field of digital humanities with the help of large language model technology. [Method/process]This paper mainly focuses on the data of CNKI journal articles, selects the general Chinese large language model GLM-4, uses prompt engineering and chain of thought to extract and cluster the abstract data, of papers and analyzes its evolution trend through quantitative processing. [Result/conclusion]The study shows that GLM-4 can well identify and extract research methods from complex abstract data. Analyzing the evolution trend in chronological order, it is found that research methods such as "interview survey" and "grounded theory" are gradually marginalized, while machine learning and other related research methods are gradually becoming mainstream. This article reveals the evolution trend of research methods in the field of Chinese digital humanities, and gives the research results of digital humanities a richer and more comprehensive cultural connotation.

First Page

65

Last Page

74

Submission Date

August 2024

Revision Date

September 2024

Acceptance Date

September 2024

Publication Date

January 2025

Digital Object Identifier (DOI)

10.19809/j.cnki.kjqbyj.2025.01.006

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