Text Analysis and Visualization Research on the Hetu Dangse During the Qing Dynasty of China


  • Zhiyu Wang Harbin institute of Technology
  • Jingyu Wu Liaoning University
  • Guang Yu Harbin institute of Technology
  • Zhiping Song Liaoning University




In traditional historical research, interpreting historical documents subjectively and manually causes problems such as one-sided understanding, selective analysis, and one-way knowledge connection. In this study, we aim to use machine learning to automatically analyze and explore historical documents from a text analysis and visualization perspective. This technology solves the problem of large-scale historical data analysis that is difficult for humans to read and intuitively understand. In this study, we use the historical documents of the Qing Dynasty Hetu Dangse,preserved in the Archives of Liaoning Province, as data analysis samples. China’s Hetu Dangse is the largest Qing Dynasty thematic archive with Manchu and Chinese characters in the world. Through word frequency analysis, correlation analysis, co-word clustering, word2vec model, and SVM (Support Vector Machines) algorithms, we visualize historical documents, reveal the relationships between functions of the government departments in the Shengjing area of the Qing Dynasty, achieve the automatic classification of historical archives, improve the efficient use of historical materials as well as build connections between historical knowledge. Through this, archivists can be guided practically in historical materials’ management and compilation.

Author Biographies

Zhiyu Wang, Harbin institute of Technology

School of Management, Harbin institute of Technology

School of History, Liaoning University

Jingyu Wu, Liaoning University

School of History, Liaoning University

Zhiping Song, Liaoning University

School of History, Liaoning University


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How to Cite

Wang, Z., Wu, J., Yu, G., & Song, Z. (2021). Text Analysis and Visualization Research on the Hetu Dangse During the Qing Dynasty of China. Information Technology and Libraries, 40(3). https://doi.org/10.6017/ital.v40i3.13279