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

机译：清代清代Hetu Dangse的文本分析与可视化研究

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In traditional historical research, interpreting historical documents subjectively and manually causes problems such as one-sided understanding, selective analysis, and one-way knowledge connection. In this study, we aim to use machine learning to automatically analyze and explore historical documents from a text analysis and visualization perspective.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 In this study, we use the historical documents of the Qing Dynasty Hetu Dangse, preserved in the Archives of Liaoning Province, as data analysis samples.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correlation analysis, co-word clustering, word2vec model, and SVM (Support Vector Machines) algorithms, we visualize historical documents, reveal the relationships between functions of the government departments in the Shengjing area of the Qing Dynasty, achieve the automatic classification of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.
机译：在传统的历史研究中，主观地解释历史文件并手动地引起片面理解，选择性分析和单向知识联系等问题。在这项研究中，我们的目标是使用机器学习从文本分析和可视化视角自动分析和探索历史文档。该技术解决了大规模的历史数据分析问题，这对于人类难以阅读和直观地了解。在这项研究中，我们使用清代Hetu Dangse的历史文件，保存在辽宁省档案中，作为数据分析样本。中国的Hetu Dangse是最大的清朝主题档案馆，拥有满族和世界汉字。通过Word频率分析，相关分析，共同词组，Word2VEC模型和SVM（支持向量机）算法，我们可视化历史文件，揭示了清代嵊泾地区政府部门职能之间的关系，实现了历史档案的自动分类，改善历史材料的有效利用以及历史知识之间的建立联系。通过这一点，档案馆几乎可以在历史材料的管理和汇编中引导。

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