文献类型：期刊文献

中文题名：美国科技智库电子信息领域报告主题挖掘与演变研究

英文题名：The Changing Landscape of US Technology Think Tanks Reports on the Electronic Information Research and Industry:A Topic Mining Perspective

作者：薛倩[1];赵宏[2];任福兵[1,3,4]

机构：[1]华东理工大学商学院,上海200237;[2]华东理工大学外国语学院,上海200237;[3]华东理工大学马克思主义学院,上海200237;[4]华东理工大学马克思主义研究院,上海200237

年份：2025

卷号：37

期号：10

起止页码：78

中文期刊名：农业图书情报学报

外文期刊名：Journal of Library and Information Science in Agriculture

基金：2023年度教育部人文社会科学研究一般项目“网络语境中Z世代青年群体价值观形成逻辑及培育策略”(23YJA710044);2024年度上海外教社信息技术有限公司教育部产学研协同育人项目“AI辅助的跨文化多元读写能力提升实践”。

语种：中文

中文关键词：美国科技智库;电子信息;BERTopic;主题挖掘;中美科技竞争

外文关键词：US technology think tanks;electronic information research and industry;BERTopic;topic mining;Sino-US tech race

摘要：[目的/意义]在中美科技竞争持续加剧的背景下,美国科技智库在电子信息领域的研究动向不仅反映其技术关注重点,也映射出其对华战略方向。对其开展量化分析,可为中国科技智库建设和战略应对提供决策支撑。[方法/过程]基于TTCSP全球智库排名及研究影响力,遴选8家美国顶尖科技智库,采集其2015—2024年间发布的1360份电子信息领域报告;融合BERTopic主题模型与时间序列分析,以实现对研究主题语义深度与演化趋势的综合挖掘,系统剖析研究主题演化路径,并重点识别涉华议题。[结果/结论]研究揭示:美国科技智库持续聚焦半导体与微电子、人工智能、无线通信、量子信息、网络安全、大数据等技术方向的11大研究方向和31个研究主题。涉华研究呈现重要战略转向:涉及半导体管制、AI竞赛、数字竞争等,映射出遏制中国技术崛起的战略意图。建议中国科技智库和相关部门构建动态监测体系,强化技术预见能力以应对复杂博弈。
[Purpose/Significance]Science and technology have emerged as pivotal domains of competition between China and the United States.This article provides a quantitative analysis of US technology think tanks reports on the electronic information research and industry,with a focus on the evolution of themes and topics over the past decade.This analysis not only reflects their technological priorities but also maps their analytical focus on China,providing decision-making support for China's think tanks development and strategic response.[Method/Process]Based on the"2020 Global Go to Think Tank Index Report"released by the Think Tanks and Civil Societies Program(TTCSP)at the University of Pennsylvania,considering factors such as think tank authority,research topic relevance,and research continuity,we collected a total of 1360 reports on the electronic information research and industry published between 2015 and 2024 by 8 leading US technology think tanks.Topic analysis was conducted with BERTopic,a topic modeling tool based on Transformer embeddings.The methodology involved several key steps.First,text cleaning was performed using NLTK tools;then,the all-MiniLM-L6-v2 model was employed to generate high-dimensional document embedding vectors.Subsequently,dimensionality reduction was achieved through the UMAP algorithm,followed by density clustering using the HDBSCAN algorithm.Finally,topic words were extracted based on the c-TF-IDF algorithm.[Results/Conclusions]The research identified 31 distinct research themes,of which 6 were directly related to China,specifically:global semiconductor industry competition,Sino-US digital policies and cloud computing competition,5G network and technology competition,Chinese AI investment,Sino-US science and innovation policies,and Sino-US military technology competition.These 31 research themes were hierarchically clustered using HDBSCAN and could be categorized into 11 major research directions.The US technology think tanks persistently focused on 11 major research directions,which were largely concentrated on key areas of electronic information research and industry,such as semiconductors and microelectronics,artificial intelligence,wireless communication,quantum information technology,network security,and big data.The evolutionary trends across these research directions were generally consistent,with military technology and network security receiving the highest level of attention.The attention attached to China has undergone a significant strategic shift over the years,with drastic increase in semiconductor export control,AI technology and Sino-US digital competition.Based on the identified key themes and topic words,it is highly recommended to establish an evolutionary mapping of China-related topics and to develop a dynamic monitoring and early warning mechanism for technology issues concerning China.Future research could incorporate larger-scale corpus resources and more advanced large language models to continuously optimize topic modeling effectiveness.