A Bibliometric Study of Chinese Official Document Writing (2005-2024)

A systematic review of the literature on official document writing helps clarify the overall research landscape and provides a reference for future studies. Using bibliometric methods, this study analyzes 2,074 valid publications on “official document writing” and related topics indexed in CNKI from 2005 to 2024. CiteSpace was employed for knowledge mapping and visualization to examine the knowledge structure, evolutionary trajectory, and collaboration patterns of Chinese official document writing research, thereby addressing the limitations of traditional qualitative reviews in quantitative analysis and temporal trend identification. The findings show that the annual publication volume followed a three-stage pattern of growth, plateau, and decline, reaching its peak in 2012 before gradually decreasing. The author collaboration network had a density of only 0.0005, with no collaborative links among the top ten most prolific authors and only 13 authors publishing more than 10 papers, indicating a predominance of individual research and a lack of stable collaborative groups. Similarly, the institutional collaboration network showed a low density of 0.0003, with only 11 links among 272 nodes, suggesting that universities are the main contributors but inter-institutional and cross-sector cooperation remains weak. Keyword co-occurrence and clustering analyses (Q=0.8283, S=0.9905) identified four major themes: the standardization of administrative official documents, official document language, writing competence cultivation, and the contemporary value of ancient official documents. Burst keyword analysis further shows that intelligent themes such as “official document communication” have become increasingly prominent since 2015.The main contribution of this study is its quantitative confirmation of the “high output but low collaboration” structure of Chinese official document writing research. It also suggests that future studies are likely to move toward large language model-assisted writing, human-machine collaboration, and semantic intelligence, providing empirical evidence for research evaluation, interdisciplinary development, and policy guidance in this field.