[Purpose/Significance] Potential emerging technologies are prone to recognition difficulties due to their inherent fuzzy characteristics. This article aims to construct a recognition system that is adapted to their fuzzy characteristics, achieve effective recognition of potential emerging technologies, and provide support for the allocation of technological innovation resources and future industrial development. [Method/Process] Based on the fuzziness characteristics of potential emerging technologies and their manifestations, using patents, news, and reports as data sources, a recognition model is constructed from two feature dimensions: technical semantic fuzziness and application scenario fuzziness. Firstly, based on dynamic networks and fuzzy C-means clustering methods, cross-domain association strength and attribution uncertainty indicators are constructed to identify semantic fuzziness technology. Secondly, with the help of the BERTopic topic model, a fuzzy technology for identifying application scenarios based on technology application diversity and scenario transition indicators is constructed. Finally, assign weights are assigned to the comprehensive technical semantic ambiguity index and the comprehensive application scenario ambiguity index to identify potential emerging technologies. [Result/Conclusion] With the field of hydrogen fuel cells as the empirical object, based on the Pareto Principle, the top 20% percentile of the comprehensive score (i.e., comprehensive potential score ≥ 0.32) is set as the recognition threshold, and finally 5 potential emerging technologies are identified.
[1] Critical and emerging technologies list 2024 update | OSTP[EB/OL]. (2024-02-12)[2025-12-18]. https://bidenwhitehouse.archives.gov/ostp/news-updates/2024/02/12/critical-and-emerging-technologies-list-2024-update/.
[2] 中华人民共和国中央人民政府. 中华人民共和国国民经济和社会发展第十四个五年规划和2035年远景目标纲要_滚动新闻_中国政府网[EB/OL]. [2025-12-18]. https://www.gov.cn/xinwen/2021-03/13/content_5592681.htm. (Central People’s Government of the People’s Republic of China. The 14th five year plan for national economic and social development of the People’s Republic of China and the 2035 long range objectives outline[EB/OL]. [2025-12-18]. https://www.gov.cn/xinwen/2021-03/13/content_5592681.htm.)
[3] ZADEH L A. Fuzzy sets[J]. Information and control, 1965, 8(3): 338-353.
[4] MAMDANI E H. Application of fuzzy algorithms for control of simple dynamic plant[J]. Proceedings of the Institution of Electrical Engineers, 1974, 121(12): 1585.
[5] 袁润, 钱过. 识别核心专利的粗糙集理论模型[J]. 图书情报工作, 2015, 59(2): 123-130. (YUAN R, QIAN G. Core patent identification based on rough set theory[J]. Library and information service, 2015, 59(2): 123-130.)
[6] 邓胜利, 林艳青, 王野. 企业竞争弱信号的特征提取与定量识别研究[J]. 图书情报工作, 2016, 60(10): 67-75. (DENG S L, LIN Y Q, WANG Y. Research on the features and quantitative identification of competitive weak signals in firms[J]. Library and information service, 2016, 60(10): 67-75.)
[7] 吴艳玲, 宋敏, 孙思阳. 基于模糊综合评价的期刊质量评价分析[J]. 情报科学, 2021, 39(9): 74-79. (WU Y L, SONG M, SUN S Y. Analysis of journal quality evaluation based on AHP and FCE method[J]. Information science, 2021, 39(9): 74-79.)
[8] 陆泉, 曹越, 陈静. 基于语义关联与模糊聚类的共词分析方法[J]. 情报学报, 2022, 41(10): 1003-1014. (CHEN Q, CAO Y, CHEN J. A co-word analysis method based on semantic relevance and fuzzy clustering[J]. Journal of the China Society for Scientific and Technical Information, 2022, 41(10): 1003-1014.)
[9] 曹树金, 赵浜. 创新视角的论文间关联挖掘研究[J]. 现代情报, 2022, 42(12): 17-27. (CAO S J, ZHAO B. Research on correlation mining between papers from the perspective of innovation[J]. Journal of modern information, 2022, 42(12): 17-27.)
[10] 谈毅, 黄燕丽. 基于过程的新兴技术规划与选择模型研究[J]. 科技管理研究, 2007(8): 5-8. (TAN Y, HUANG Y L. Research on process based planning and selection model for emerging technologies[J]. Science and technology management research, 2007(8): 5-8.)
[11] TRAN T A, DAIM T. A taxonomic review of methods and tools applied in technology assessment[J]. Technological forecasting and social change, 2008, 75(9): 1396-1405.
[12] 白如江, 刘博文, 冷伏海. 基于多维指标的未来新兴科学研究前沿识别研究[J]. 情报学报, 2020, 39(7): 747-760. (BAI R J, LIU B W, LENG F H. Frontier identification of emerging scientific research based on multi-indicators[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(7): 747-760.)
[13] JIANG M, YANG S, GAO Q. Multidimensional indicators to identify emerging technologies: perspective of technological knowledge flow[J]. Journal of informetrics, 2024, 18(1): 101483.
[14] 张凯, 吕璐成, 韩涛, 等. “论文—专利”关联视角下的新兴技术识别研究[J]. 情报理论与实践, 2024, 47(9): 183-191. (ZHANG K, LV L C, HAN T, et al. Research on emerging technology identification from the perspective of “paper-patent” correlation[J]. Information studies: theory & application, 2024, 47(9): 183-191.)
[15] CHOI D, SONG B. Exploring technological trends in logistics: topic modeling-based patent analysis[J]. Sustainability, 2018, 10(8): 2810.
[16] XIANG M, FU D, LV K. Identifying and predicting trends of disruptive technologies: an empirical study based on text mining and time series forecasting[J]. Sustainability, 2023, 15(6): 5412.
[17] SONG B, LUAN C, LIANG D. Identification of emerging technology topics (ETTs) using BERT-based model and sematic analysis: a perspective of multiple-field characteristics of patented inventions (MFCOPIs)[J]. Scientometrics, 2023, 128(11): 5883-5904.
[18] SMALL H, BOYACK K W, KLAVANS R. Identifying emerging topics in science and technology[J]. Research policy, 2014, 43(8): 1450-1467.
[19] DE PAULO A F, NUNES B, PORTO G. Emerging green technologies for vehicle propulsion systems[J]. Technological forecasting and social change, 2020, 159: 120054.
[20] 郑荣, 魏明珠, 高志豪, 等. 基于SCAN-CPM的产业新兴技术识别与演化路径分析：以新能源汽车产业为例[J]. 图书情报工作, 2022, 66(11): 100-109. (ZHENG R, WEI M Z, GAO Z H, et al. SCAN-CPM-based industry emerging technology identification and evolution path analysis: taking the new energy automobile industry as an example[J]. Library and information service, 2022, 66(11): 100-109.)
[21] ROTOLO D, HICKS D, MARTIN B R. What is an emerging technology?[J]. Research policy, 2015, 44(10): 1827-184.
[22] ANSOFF H I. Managing strategic surprise by response to weak signals[J]. California management review, 1975, 18(2): 21-33.
[23] HILTUNEN E. The future sign and its three dimensions[J]. Futures, 2008, 40(3): 247-260.
[24] 韩盟, 陈悦, 王玉奇, 等. 新兴技术弱信号识别：理论模型与测度方法[J]. 科学学研究, 2024, 42(11): 2262-2274. (HAN M, CHEN Y, WANG Y Q, et al. Identification of weak signals of emerging technologies: theoretical model and measurement method[J]. Studies in science of science, 2024, 42(11): 2262-2274.)
[25] BEZDEK J C, EHRLICH R, FULL W. FCM: the fuzzy c-means clustering algorithm[J]. Computers & geosciences, 1984, 10(2): 191-203.
[26] HU F, CHEN H, WANG X. An intuitionistic kernel-based fuzzy c-means clustering algorithm with local information for power equipment image segmentation[J]. IEEE Access, 2020, 8: 4500-4514.
[27] RASHID J, SHAH S M A, IRTAZA A. Fuzzy topic modeling approach for text mining over short text[J]. Information processing & management, 2019, 56(6): 102060.
[28] JEON E, YOON N, SOHN S Y. Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and patentSBERTa[J]. Technological forecasting and social change, 2023, 186: 122130.
[29] LU C, QIAO J, CHANG J. Herfindahl–hirschman index based performance analysis on the convergence development[J]. Cluster computing, 2017, 20(1): 121-129.
[30] SAMPSON J. Hydrogen insights 2023[EB/OL]. [2025-12-18]. https://hydrogencouncil.com/en/hydrogen-insights-2023/.
