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基于AutoPhrase-SBERT论文-专利文本挖掘的技术演化研究

A Study of Technological Evolution of Paper-patent Text Mining Based on AutoPhrase - SBERT

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【作者】 刘晋霞柴福厚董世庆

【Author】 Liu Jinxia;Chai Fuhou;Dong Shiqing;School of Economics and Management, Taiyuan University of Science and Technology;Shanxi Provincial Department of Water Resources;

【通讯作者】 柴福厚;

【机构】 太原科技大学经济与管理学院山西省水利厅

【摘要】 [目的/意义]通过对制氢领域的论文和专利文本分析,揭示该领域的技术演化趋势,为科研人员、工程师了解该领域的关键技术和发展趋势提供参考。[方法/过程]采用AutoPhrase-SBERT算法对2000—2022年期间我国制氢领域的论文和专利进行主题提取与识别。通过皮尔逊相关系数对相邻时间窗口下论文-专利主题进行关联计算,并利用桑基图对主题关联性进行可视化,从而揭示我国制氢领域技术演变过程。[结果/结论]该方法能够展示领域技术随时间的变化趋势,并能揭示论文和专利主题之间的关系和相互影响。

【Abstract】 [Purpose/significance] Through the analysis of the papers and patent texts within the hydrogen production field, this paper reveals the technological evolution trend, which provides references for researchers and engineers to understand the key technologies and development trends in this field. [Method/process] This paper uses the AutoPhrase-SBERT algorithm to extract and identify the topics of papers and patents in the field of hydrogen production from 2000 to 2022 in China. And it employs Pearson correlation coefficients to calculate the correlation between the paper and patent subject under the adjacent time window, and employs Sankey diagrams to visualize the topic correlation, so as to reveal the technological evolution in the field of hydrogen production in China. [Result/conclusion] This method can show the changing trend of the technology in this field over time, and can reveal the relationship and interaction between the paper and patent topics.

【关键词】 AutoPhraseSBERT文本挖掘技术演化
【Key words】 AutoPhraseSBERTtext miningtechnological evolution
【基金】 山西省教学改革创新项目“案例驱动式Python数据分析教学内容改革的探索与实践”(项目编号:JG2023092);山西省软科学项目“基于复杂网络数据驱动的山西省科技计划项目智能监督评价模型研究”(项目编号:2018041046-4)成果
  • 【文献出处】 情报探索 ,Information Research , 编辑部邮箱 ,2024年08期
  • 【分类号】G255.53;TQ116.2
  • 【下载频次】5
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