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Journal of library and information science in agriculture ›› 2026, Vol. 38 ›› Issue (7): 82-96.doi: 10.13998/j.cnki.issn1002-1248.25-0665

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Multidimensional Market Demand Theme Identification and Evolution Analysis of Potential Disruptive Technologies

WANG Song1,2, PAN Yuanyuan1,2   

  1. 1.College of Economics and Management, Shandong University of Science and Technology, Qingdao 266590
    2.Institute of Organizational Governance and Competition, Shandong University of Science and Technology, Qingdao 266590
  • Received:2025-11-21 Online:2026-07-05 Published:2026-07-13

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Abstract:

[Purpose/Significance] Disruptive technologies are a core force reshaping the industrial landscape, but their inherent market uncertainty contradicts traditional management logic, posing a significant challenge to the resource allocation decisions of innovation entities. To address this issue, this study, starting from the market characteristics of disruptive technologies, utilizes a hierarchical analysis framework and combines deep learning methods to identify multidimensional market demand themes for potential disruptive technologies and conduct evolutionary analysis. This aims to provide a reliable basis for strategic decision-making and resource allocation by various innovation entities, moving from "experience and intuition" to "scientific foresight." [Method/Process] Based on the substitutive market characteristics of disruptive technologies, a hierarchical analysis framework of "substitutability assessment - multi-entity demand mining - deep clustering" was constructed to identify and analyze multi-dimensional demand market themes based on potential disruptive technologies. First, the set of potential disruptive technologies that has been widely defined in existing research was systematically reviewed. Based on this, an innovation diffusion model was used to quantitatively assess their market substitutability, thereby identifying disruptive technologies with market substitution potential. Secondly, based on the identified technologies with market substitutability, and considering the demand-driven, technology transfer, and institutional guarantee mechanisms for disruptive technology market applications, this study explores multi-dimensional demand content from multiple perspectives, including users, enterprises, and government. It integrates various deep learning methods, such as user demand analysis based on multi-dimensional feature fusion, enterprise demand analysis based on text similarity networks, and government demand analysis based on data augmentation, to differentiate and mine multi-dimensional demand content. Finally, based on the mined multi-dimensional demand content, deep clustering was used to identify core market demand themes for disruptive technologies from multi-source data from users, enterprises, and government, and to analyze their dynamic evolution patterns. [Results/Conclusions] Taking the field of artificial intelligence as an example, this empirical study identified 30 potential disruptive technology market demand themes for 2021-2025, covering global digital trade technology, online behavior governance technology, intelligent waste sorting technology, intelligent transportation technology, intelligent voice interaction technology, digital cultural tourism technology, green technology innovation, green city construction technology, and smart logistics technology. The identified results have been verified by global policy documents and expert authorities, and are highly consistent with the development trends of potential disruptive technologies, effectively echoing the core directions of the current national science and technology innovation strategy and industrial transformation and upgrading. However, this study only focuses on the field of artificial intelligence and does not comprehensively cover different technological fields. Future work will extend to other technological fields to test and improve the general theory of identifying disruptive technology market themes.

Key words: potentially disruptive technologies, market characteristics, hierarchical structure, multidimensional market demand, theme identification, evolutionary analysis

CLC Number: 

  • G353.1

Fig.1

Technology roadmap"

Table 1

Multi-source intelligence datasets around disruptive technology characteristics"

视角维度数据来源
市场替代性特征市场规模企查查数据库
各创新主体公众感知微博社交媒体平台
市场环境

科惠网、贤集网、技E网、InnoMatch等技术需求发布平台

前程无忧、智联招聘、58同城等招聘信息发布平台

政策导向北大法宝网、中国政府网等政策发布平台

Table 2

Market scale data"

潜在颠覆性技术2010201120122013...2022202320242025
新一代互联通信技术43 00850 71255 51055 510...295 830446 021393 352207 581
智能计算与数字处理技术63 44378 14585 217115 477...978 1261 404 0411 309 318570 326
智能环境保护技术8 38210 42311 13914 994...122 869142 851123 92067 068
智能工业制造技术4 1634 7674 7236 149...87 988105 39599 68056 735
智能交通技术3 5574 3914 8046 950...38 88146 96239 44920 139
智能语音技术212262248316...15313410922
智慧储能技术4 0964 6104 4415 393...1 011 44138 502149 70081 013
智慧医疗技术6411292121...287267219103

Table 3

Regression results"

潜在颠覆性技术新一代互联通信技术智能计算与数字处理技术智能环境保护技术智能工业制造技术
p0.007 990 2340.001 244 5390.000 769 667-6.89E-03
q0.253 028 9890.364 428 9880.409 428 9170.450 611 78
m5 056 468.59413 384 332.641 465 927.09834 305.769 2
p0.001 584 8220.000 172 421-0.007 913 106-0.016 082 792
q0.393 864 0180.499 672 5530.473 161 0460.642 808 038
m503 535.602 46 488.885 5691 058 656.3647 004.486 878

Table 4

Weibo data retrieval strategy"

项目内容
检索平台微博
时间范围2021年1月1日—2025年6月30日
检索关键词

“通信架构*算力网络*智能组网”

“智能计算*人工智能引擎*自动化信息处理”

“智能环境保护*智能环保*绿色智能”

“智能交通*智慧交通*数字化交通”

“智能语音*语音交互*语音识别”

Table 5

Weights of indicator combinations for each year"

指标20212022202320242025
转发数0.220.220.220.220.22
点赞数0.160.180.160.180.16
评论数0.180.180.180.200.18
情感得分0.510.500.520.520.52

Table 6

Network structure under different thresholds"

年份阈值网络直径平均聚类系数边数/条
20210.640.355 8193 363
0.760.374 6177 752
0.8120.404 7108 227
0.9130.228 76 991
20220.640.323 0300 139
0.770.342 9275 825
0.8140.382 5170 849
0.9260.180 04 111
20230.640.366 1150 885
0.750.385 4137 409
0.890.417 874 518
0.9170.141 81 372
20240.660.317 0261 595
0.770.330 9244 720
0.8100.376 7146 011
0.9230.136 52 619
20250.650.413 391 897
0.770.431 383 942
0.890.444 934 591
0.9150.111 0599

Table 7

Comparison of recognition effects of common deep learning models"

模型准确率（Acc）精确率（Pre）召回率（Re）F1-Score
BiLSTM0.963 70.963 40.963 30.963 4
BiLSTM-Attention0.961 50.962 20.960 10.961 0
TextCNN0.971 90.971 90.971 30.971 6
RF0.955 60.957 00.953 50.955 0
SVM0.921 50.920 10.922 20.921 0

Fig.2

Cluster analysis in 2021"

Fig.3

Cluster analysis in 2022"

Fig.4

Cluster analysis in 2023"

Fig.5

Cluster analysis in 2024"

Fig.6

Cluster analysis in 2025"

Table 8

Comparison of the effects of two models"

模型评估系数模型20212022202320242025
CH指标BERT-DTM222.11784.68254.12467.67507.28
SBERT-DTM538.82292.24189.37904.45189.08

Fig.7

SBERT-DTM clustering diagram in 2021"

Fig.8

BERT-DTM clustering diagram in 2022"

Fig.9

BERT-DTM clustering diagram in 2023"

Fig.10

SBERT-DTM clustering diagram in 2024"

Fig.11

BERT-DTM clustering diagram in 2025"

Fig.12

Sankey diagram of the evolution of market demand themes, 2021-2025"

Table 9

Correspondence between the themes of "technology-demand""

新一代互联通信技术智能计算与数字处理技术智能环境保护技术智能交通技术智能语音交互技术
5G网络安全、智慧城市、智慧民航、全球数字经贸、跨境数字贸易、网络安全技术应用、网络行为治理专业技术研发、企业数字化创新、企业数字化消费洞察、数字消费生态、公共服务数字化、政务信息管理、数据安全治理、数字文旅、科技创新国际合作、个人信息保护、智能高考填报、智能亲子鉴定、智能防疫、农业数字金融绿色技术创新、智能垃圾分类、乡村生态、绿色城市建设、新能源制造智能交通、智能驾驶、智慧物流

智能语音交互、新一代智能音箱

[1] Govindarajan V, Kopalle P K. Disruptiveness of innovations: Measurement and an assessment of reliability and validity[J]. Strategic Management Journal, 2006, 27(2): 189-199.
[2] 王康, 陈悦, 宋超, 等. 颠覆性技术: 概念辨析与特征分析[J]. 科学学研究, 2022, 40(11): 1937-1946.
Wang Kang, Chen Yue, Song Chao, et al. Disruptive technology: Concept discrimination and characteristics analysis[J]. Studies in Science of Science, 2022, 40(11): 1937-1946.
[3] Bergek A, Berggren C, Magnusson T, et al. Technological discontinuities and the challenge for incumbent firms: Destruction, disruption or creative accumulation?[J]. Research Policy, 2013, 42(6/7): 1210-1224.
[4] 曲冠楠, 陈凯华, 陈劲. 颠覆性技术创新: 理论源起、整合框架与发展前瞻[J]. 科研管理, 2023, 44(9): 1-9.
Qu Guannan, Chen Kaihua, Chen Jin. Disruptive technovation: Origins, integrated framework, and prospects[J]. Science Research Management, 2023, 44(9): 1-9.
[5] 许佳琪, 汪雪锋, 雷鸣, 等. 从突破性创新到颠覆性创新: 内涵、特征与演化[J]. 科研管理, 2023, 44(2): 1-13.
Xu Jiaqi, Wang Xuefeng, Lei Ming, et al. From breakthrough innovation to disruptive innovation: Their connotations, characteristics and evolution[J]. Science Research Management, 2023, 44(2): 1-13.
[6] 苏成, 赵志耘, 赵筱媛, 等. 颠覆性技术新阐释: 概念、内涵及特征[J]. 情报学报, 2021, 40(12): 1253-1262.
Su Cheng, Zhao Zhiyun, Zhao Xiaoyuan, et al. New explanation on disruptive technology: Concept, connotation and features[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(12): 1253-1262.
[7] 孙永福, 王礼恒, 孙棕檀, 等. 引发产业变革的颠覆性技术内涵与遴选研究[J]. 中国工程科学, 2017, 19(5): 9-16.
Sun Yongfu, Wang Liheng, Sun Zongtan, et al. Connotation and selection of disruptive technologies that lead industrial change[J]. Engineering Science, 2017, 19(5): 9-16.
[8] 张金柱, 张晓林. 基于被引科学知识主题突变的突破性创新识别[J]. 现代图书情报技术, 2016, (Z1): 42-50.
Zhang Jinzhu, Zhang Xiaolin. Radical innovation identification based on topic mutation of scientific knowledge cited in patents[J]. Data Analysis and Knowledge Discovery, 2016, (Z1): 42-50.
[9] 汪雪锋, 张硕, 韩晓彤, 等. 技术预测研究现状与未来展望[J]. 农业图书情报, 2019, 31(6): 4-11.
Wang Xuefeng, Zhang Shuo, Han Xiaotong, et al. Technology forecasting: Status and trends[J]. Agricultural Library and Information, 2019, 31(6): 4-11.
[10] 张彪, 董坤, 田常伟, 等. “双链”融合视角下关键核心技术分析框架及应用研究——以山东省区块链产业为例[J]. 情报理论与实践, 2023, 46(11): 133-142.
Zhang Biao, Dong Kun, Tian Changwei, et al. Analysis framework and application research of key core technologies from the perspective of "dual chain" integration: Taking the block chain industry in Shandong Province as an example[J]. Information Studies (Theory Application), 2023, 46(11): 133-142.
[11] 安欣, 刘迪航, 徐硕. “双碳”领域颠覆性技术度量与识别——基于多源数据融合方法[J]. 科技进步与对策, 2024, 41(17): 1-11.
An Xin, Liu Dihang, Xu Shuo. Measurement and identification of disruptive technologies in the field of "dual carbon" base on the method of multi-source data fusion[J]. Science Technology Progress and Policy, 2024, 41(17): 1-11.
[12] Habtay S R. A firm-level analysis on the relative difference between technology-driven and market-driven disruptive business model innovations[J]. Creativity and Innovation Management, 2012, 21(3): 290-303.
[13] 吕鲲, 张未旭, 靖继鹏. 基于CLIP-LDAGV多模态信息融合的颠覆性技术主题识别研究——以新能源领域为例[J]. 情报学报, 2025, 44(3): 353-368.
Kun Lyu, Zhang Weixu, Jing Jipeng. Research on disruptive technology topic recognition based on CLIP-LDAGV multimodal information fusion - A case study of the new energy field[J]. Journal of the China Society for Scientific and Technical Information, 2025, 44(3): 353-368.
[14] 许海云, 王超, 陈亮, 等. 颠覆性技术的科学-技术-产业互动模式识别与分析[J]. 情报学报, 2023, 42(7): 816-831.
Xu Haiyun, Wang Chao, Chen Liang, et al. Recognition and analysis of science-technology-industry interaction patterns of disruptive technologies[J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(7): 816-831.
[15] 李宜展, 孔晔晗, 李泽霞. 可拓理论在技术演化与预测中的应用潜力[J]. 现代情报, 2024, 44(10): 86-102.
Li Yizhan, Kong Yehan, Li Zexia. Potential application of extenics in technology evolution and forecasting[J]. Modern Information, 2024, 44(10): 86-102.
[16] 徐宗煌, 李小乐, 石进, 等. 基于多源异构数据的关键核心技术识别研究——以光刻技术为例[J]. 中国科技论坛, 2024(12): 127-136, 164.
Xu Zonghuang, Li Xiaole, Shi Jin, et al. Identifying key core technologies using multi-source heterogeneous data - A case study on lithography technology[J]. Forum on Science and Technology in China, 2024(12): 127-136, 164.
[17] 邓启平, 柯佳秀. 基于基金项目数据的新兴交叉主题识别——以量子技术为例[J]. 图书情报工作, 2023, 67(20): 130-141.
Deng Qiping, Ke Jiaxiu. Identifying emerging interdisciplinary topics based on the fund project data: A case study of quantum technology[J]. Library and Information Service, 2023, 67(20): 130-141.
[18] Beheshti M, ZareRavasan A, Mahdiraji H A, et al. An overview of the consumer-centric disruptive technology research: Insights from topic modelling and literature review[J]. Journal of Consumer Behaviour, 2024, 23(2): 372-388.
[19] 张宝建, 李鹏利, 陈劲, 等. 国家科技创新政策的主题分析与演化过程——基于文本挖掘的视角[J]. 科学学与科学技术管理, 2019, 40(11): 15-31.
Zhang Baojian, Li Pengli, Chen Jin, et al. Thematic analysis and evolution process of national science and technology innovation policy: Based on the perspective of text mining[J]. Science of Science and Management of S T (Monthly), 2019, 40(11): 15-31.
[20] 刘婷, 赵亚娟. 技术机会识别研究综述与展望[J]. 农业图书情报学报, 2023, 35(7): 4-17.
Liu Ting, Zhao Yajuan. Review and prospect of research on technology opportunity identification[J]. Journal of Library and Information Science in Agriculture, 2023, 35(7): 4-17.
[21] 姚加权, 张锟澎, 郭李鹏, 等. 人工智能如何提升企业生产效率?——基于劳动力技能结构调整的视角[J]. 管理世界, 2024, 40(2): 101-116, 133, 117-122.
Yao Jiaquan, Zhang Kunpeng, Guo Lipeng, et al. How does artificial intelligence improve firm Productivity? Based on the perspective of labor skill structure adjustment[J]. Journal of Management World, 2024, 40(2): 101-116, 133, 117-122.
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