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作 者:白如江[1] 任前前 陈鑫[1] Bai Rujiang;Ren Qianqian;Chen Xin(School of Information Management,Shandong University of Technology,Zibo 255049,China)
机构地区:[1]山东理工大学信息管理学院,山东淄博255049
出 处:《现代情报》2024年第2期43-54,共12页Journal of Modern Information
基 金:山东省高等学校“青创科技计划”科技大数据驱动的智慧决策支持创新团队(项目编号:2019RWG033)。
摘 要:[目的/意义]为服务国家重大战略需求,抓住学科更名的历史机遇,把握一级学科的整体发展脉络成为构建中国自主的信息资源管理知识体系的重要环节。[方法/过程]以CNKI收录的学科核心期刊文献为数据源,首先采用K-Means聚类算法以及融合TF-IDF算法和LDA主题模型的分类方法计算最佳主题数量并识别研究主题内容;然后从更名前后的主题强度、主题新颖度、研究主题发展变化情况3个方面深入分析;最后,本研究提出主题词交叉联立的分析方法预测研究主题未来发展方向。[结果/结论]信息资源管理一级学科的更名会刺激新主题的产生与发展,新主题在研究内容、研究范围等方面的拓展反过来丰富了本学科的内涵和外延,对于学科未来的守正创新发展具有一定参考价值。[Purpose/Significance]In order to serve the country's major strategic needs and seize the historical opportunity of discipline renaming,grasping the overall development context of first-level disciplines which becomes an important link in building China's independent information resource management knowledge system.[Method/process]The data source was the core journals of the discipline included in CNKI.First,the study used K-Means clustering algorithm and the classification method integrating TF-IDF algorithm and LDA Topic model to calculate the optimal number of topics and identified the content of research topics;Then,it conducted an in-depth analysis from three aspects:the><8intensity of the topic before and after the renaming,the novelty of the topic,and the development and changes of the research topic;Finally,this study proposed an analytical method for predicting the future development direction of research topics based on the intersection of topic words.[Result/Conclusion]The renaming of the first-level discipline of information resource management would stimulate the emergence and development of new topics.The expansion of new topics in research content,scope,and other aspects in turn enriches the connotation and extension of this discipline,which has certain reference value for the future development of the discipline's standardization and innovation.
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