认知视角下的科技弱信号分类空间与动态演化研究

doi:10.3772/j.issn.1000-0135.2026.04.004

情报学报

2026, Vol. 45

Issue (4): 504-519 DOI: 10.3772/j.issn.1000-0135.2026.04.004

情报理论与方法

本期目录 | 过刊浏览 | 高级检索

认知视角下的科技弱信号分类空间与动态演化研究

张慧玲¹, 许海云², 陈亮⁴, 王超², 刘春江³, 王海燕⁴

1.太原市图书馆，太原 030024
2.山东理工大学管理学院，淄博 255000
3.中国科学院成都文献情报中心，成都 610213
4.中国科学技术信息研究所，北京 100038

Classification Space and Dynamic Evolution of Technological Weak Signals from Cognitive Perspective

Zhang Huiling¹, Xu Haiyun², Chen Liang⁴, Wang Chao², Liu Chunjiang³, Wang Haiyan⁴

1.Taiyuan Library, Taiyuan 030024
2.School of Management, Shandong University of Technology, Zibo 255000
3.Chengdu Literature and Information Center, Chinese Academy of Sciences, Chengdu 610213
4.Institute of Scientific and Technical Information of China, Beijing 100038

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (3881 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要本文旨在解析科技弱信号的认知特征与认知过程，构建面向技术预见的弱信号认知空间与多种信号类型的转化框架模型，实现弱信号主题的演化监测，推进新兴前沿技术的早期识别。首先，归纳分析了科技弱信号内涵及特征，解析科技弱信号识别的认知特征；其次，结合认知与演化视角，构建面向前沿新兴技术识别与预见的信号认知空间，形成认知视角下的全谱段的信号分类，探究多类型弱信号的特征差异及其转化路径；最后，构建感知与认知结合下的“双锥”模型，实现信号分类与转化路径分析。以干细胞领域的案例分析验证了本文提出的信号分类空间与演化分析模型的可行性。该模型依托构建的科技弱信号分类空间，解析信号间转化机制与演化路径，捕捉创新领域弱信号，借分类体系与多维演化分析实现弱信号尽早识别、规避信号遗失，提升了科技弱信号识别的准确率与召回率，动态追踪其在分类空间内的状态跃迁与态势，为新兴前沿主题预判提供了信号演化规律支撑。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	张慧玲
	许海云
	陈亮
	王超
	刘春江
	王海燕

关键词 ：弱信号, 早期信号, 前沿技术, 新兴技术, 科技情报

收稿日期: 2025-03-21

基金资助:国家自然科学基金项目“基于弱信号时效网络演化分析的变革性科技创新主题早期识别方法研究”（72274113）；山东省社科规划研究项目“面向颠覆性技术早期识别的弱信号演化模式与机制研究”（23CTQJ07）；山东省泰山学者工程项目（202103069）。

作者简介: 张慧玲，1993年生，硕士研究生，研究领域为科技与产业情报分析；许海云，通信作者，1982年生，博士，教授，博士生导师，研究领域为科技与产业情报分析，E-mail：xuhaiyunnemo@gmail.com；陈亮，1982年生，博士，副研究员，硕士生导师，研究领域为自然语言处理与知识产权管理；王超，1988年生，博士，副教授，硕士生导师，研究领域为科技与产业情报分析；刘春江，1984年生，博士，高级工程师，硕士生导师，研究领域为科技文献挖掘与知识发现；王海燕，1974年生，博士，副研究员，研究领域为科学计量学；

引用本文:

张慧玲, 许海云, 陈亮, 王超, 刘春江, 王海燕. 认知视角下的科技弱信号分类空间与动态演化研究[J]. 情报学报, 2026, 45(4): 504-519.
Zhang Huiling, Xu Haiyun, Chen Liang, Wang Chao, Liu Chunjiang, Wang Haiyan. Classification Space and Dynamic Evolution of Technological Weak Signals from Cognitive Perspective. 情报学报, 2026, 45(4): 504-519.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2026.04.004 或 https://qbxb.istic.ac.cn/CN/Y2026/V45/I4/504

1 周晓纪, 张永伟, 马守磊, 等. 中国工程科技2040技术预见方法体系及其应用[J]. 中国工程科学, 2024, 26(5): 117-127.
2 Eulaerts O, Joanny G, Giraldi J, et al. Weak signals in science and technologies - 2019 report: technologies at a very early stage of development that could impact the future[R]. Luxembourg: Publications Office of the European Union, 2019: JRC118147.
3 刘亚辉, 许海云. 突破性创新早期识别与弱信号分析综述[J]. 图书情报工作, 2021, 65(4): 89-101.
4 Ma M, Mao J, Li G. Discovering weak signals of emerging topics with a triple-dimensional framework[J]. Information Processing & Management, 2024, 61(5): 103793.
5 Peng J Y, Kimmig A, Wang D K, et al. A systematic review of data-driven approaches to fault diagnosis and early warning[J]. Journal of Intelligent Manufacturing, 2023, 34(8): 3277-3304.
6 Ebadi A, Auger A, Gauthier Y. Detecting emerging technologies and their evolution using deep learning and weak signal analysis[J]. Journal of Informetrics, 2022, 16(4): 101344.
7 徐硕, 王聪聪, 安欣. 新兴技术弱信号扫描预判述评[J]. 情报杂志, 2023, 42(3): 117-122.
8 Cerme?o-Aínsa S. The perception/cognition distincton: challenging the representational account[J]. Consciousness and Cognition, 2021, 95: 103216.
9 陈桂菊. 基于群智大数据的非常规突发事件价值情报感知研究[J]. 无线互联科技, 2021, 18(1): 28-29.
10 赵志耘, 潘云涛, 苏成, 等. 颠覆性技术感知响应系统框架研究[J]. 情报学报, 2021, 40(12): 1245-1252.
11 张慧玲, 许海云, 王超, 等. 弱信号环境下情报感知方法框架研究[J]. 情报理论与实践, 2023, 46(11): 9-19.
12 赵柯然, 王延飞. 信息迷雾的情报感知研究[J]. 情报理论与实践, 2021, 44(3): 1-5, 12.
13 白沛沅, 夏一雪, 杨雨光, 等. 基于诉求词典的突发事件情报感知与实证研究[J]. 情报杂志, 2022, 41(9): 88-98.
14 Harley T. Reviews: cognition and perception: how do psychology and neural science inform philosophy?[J]. Perception, 2010, 39(3): 441-442.
15 曹海艳, 王暖臣, 穆歌, 等. 基于技术预见视角的弱信号识别研究综述[J]. 情报学报, 2025, 44(10): 1342-1358.
16 Ansoff H I. Managing strategic surprise by response to weak signals[J]. California Management Review, 1975, 18(2): 21-33.
17 Hiltunen E. The future sign and its three dimensions[J]. Futures, 2008, 40(3): 247-260.
18 Thorleuchter D, Van den Poel D. Weak signal identification with semantic web mining[J]. Expert Systems with Applications, 2013, 40(12): 4978-4985.
19 El Akrouchi M, Benbrahim H, Kassou I. End-to-end LDA-based automatic weak signal detection in web news[J]. Knowledge-Based Systems, 2021, 212: 106650.
20 Tshitoyan V, Dagdelen J, Weston L, et al. Unsupervised word embeddings capture latent knowledge from materials science literature[J]. Nature, 2019, 571(7763): 95-98.
21 Hand D J. Dark data[M]. Princeton: Princeton University Press, 2020.
22 Heidorn P B. Shedding light on the dark data in the long tail of science[J]. Library Trends, 2008, 57(2): 280-299.
23 单彬. 认知视角下的弱信号分析及实证研究[D]. 北京: 中国人民解放军军事医学科学院, 2014.
24 Yoon J. Detecting weak signals for long-term business opportunities using text mining of web news[J]. Expert Systems with Applications, 2012, 39(16): 12543-12550.
25 Kuosa T. Different approaches of pattern management and strategic intelligence[J]. Technological Forecasting and Social Change, 2011, 78(3): 458-467.
26 Kim J, Park Y, Lee Y. A visual scanning of potential disruptive signals for technology roadmapping: investigating keyword cluster, intensity, and relationship in futuristic data[J]. Technology Analysis & Strategic Management, 2016, 28(10): 1225-1246.
27 党倩娜. 新兴技术弱信号监测机制研究[M]. 上海: 上海科学技术文献出版社, 2018.
28 Rossel P. Weak signals as a flexible framing space for enhanced management and decision-making[J]. Technology Analysis & Strategic Management, 2009, 21(3): 307-320.
29 董尹, 刘千里, 宋继伟, 等. 弱信号研究综述: 概念、方法和工具[J]. 情报理论与实践, 2018, 41(10): 147-154.
30 Garcia-Nunes P I, da Silva A E A. Using a conceptual system for weak signals classification to detect threats and opportunities from web[J]. Futures, 2019, 107: 1-16.
31 Kaivo-oja J. Weak signals analysis, knowledge management theory and systemic socio-cultural transitions[J]. Futures, 2012, 44(3): 206-217.
32 Schoemaker P J H, Day G S, Snyder S A. Integrating organizational networks, weak signals, strategic radars and scenario planning[J]. Technological Forecasting and Social Change, 2013, 80(4): 815-824.
33 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.
34 Endsley M R. Final reflections: situation awareness models and measures[J]. Journal of Cognitive Engineering and Decision Making, 2015, 9(1): 101-111.
35 Kim J, Lee C. Novelty-focused weak signal detection in futuristic data: assessing the rarity and paradigm unrelatedness of signals[J]. Technological Forecasting and Social Change, 2017, 120: 59-76.
36 Park C, Cho S, Heo W. Study on the future sign detection in areas of academic interest related to the digitalization of the energy industry[J]. Journal of Cleaner Production, 2021, 313: 127801.
37 MacDonald J, Bath P, Booth A. Information overload and information poverty: challenges for healthcare services managers?[J]. Journal of Documentation, 2011, 67(2): 238-263.
38 Tsao J Y, Abbott R G, Crowder D C, et al. AI for technoscientific discovery: a human-inspired architecture[J]. Journal of Creativity, 2024, 34(2): 100077.
39 Saffo P. Six rules for effective forecasting[J]. Harvard Business Review, 2007, 85(7/8): 122, 124, 126-131.
40 Milovidov V. Hearing the sound of the wave: what impedes one’s ability to foresee innovations?[J]. Foresight and STI Governance, 2018, 12(1): 88-97.
41 张慧玲, 许海云, 刘春江, 等. 科技创新弱信号早期感知方法探究与前瞻[J]. 情报学报, 2024, 43(10): 1129-1141.
42 Gl?ser J. How can governance change research content? Linking science policy studies to the sociology of science[M]// Handbook on Science and Public Policy. Cheltenham: Edward Elgar Publishing, 2019: 419-447.
43 Gl?ser J, Laudel G, Grieser C, et al. Scientific fields as epistemic regimes: new opportunities for comparative science studies[EB/OL]. https://www.ssoar.info/ssoar/bitstream/handle/document/60196/ssoar-2018-glaser_et_al-Scientific_fields_as_epistemic_regimes.pdf?sequence=1&isAllowed=y&lnkname=ssoar-2018-glaser_et_al-Scientific_fields_as_epistemic_regimes.pdf.
44 Logan D C. Known knowns, known unknowns, unknown unknowns and the propagation of scientific enquiry[J]. Journal of Experimental Botany, 2009, 60(3): 712-714.
45 Whitley R. The intellectual and social organization of the sciences[M]. Oxford: Oxford University Press, 2000.
46 Lee K, Jung K, Yang J S. From weak to strong signals: exploring R&D projects with research equipment[J]. Journal of Informetrics, 2025, 19(4): 101747.
47 Donnelly H K, Han Y, Song J Y, et al. Application of social big data to identify trends of school bullying forms[J]. International Journal of Environmental Research and Public Health, 2019, 16(14): 2596.
48 Xia W J, Li T R, Li C S. A review of scientific impact prediction: tasks, features and methods[J]. Scientometrics, 2023, 128(1): 543-585.
49 Xu H Y, Yue Z H, Pang H S, et al. Integrative model for discovering linked topics in science and technology[J]. Journal of Informetrics, 2022, 16(2): 101265.
50 Xu H Y, Winnink J, Yue Z H, et al. Topic-linked innovation paths in science and technology[J]. Journal of Informetrics, 2020, 14(2): 101014.
51 Gl?ser J, Heinz M, Havemann F. Epistemic diversity as distribution of paper dissimilarities[C]// Proceedings of ISSI 2015 Istanbul: 15th International Society of Scientometrics and Informetrics Conference. Istanbul: Bo?azi?i University Printhouse, 2015: 1006-1017.
52 孟宪会. 基于miRNA的间充质干细胞干性维持机制及应用研究[D]. 南京: 东南大学, 2018.
53 褚启龙. 氧化应激与细胞凋亡关系的研究进展[J]. 卫生研究, 2003, 32(3): 276-279.
54 黎美章, 章卫平, 王健民, 等. 移植物抗宿主病对白血病异基因外周血干细胞移植预后的影响[J]. 中华血液学杂志, 2014, 35(5): 428-433.
55 罗林杰, 曾健, 田朝霞, 等. 植物的发育: 从细胞到个体[J]. 科学通报, 2016, 61(33): 3532-3540.
56 朱宗财, 王志军, 高能, 等. CRISPR/Cas9基因编辑技术在植物抗病性改良中的应用综述[J]. 江苏农业科学, 2024, 52(3): 1-11.