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Journal of library and information science in agriculture

   

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:2026-04-08

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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 2010 2011 2012 2013 ... 2022 2023 2024 2025
新一代互联通信技术 43 008 50 712 55 510 55 510 ... 295 830 446 021 393 352 207 581
智能计算与数字处理技术 63 443 78 145 85 217 115 477 ... 978 126 1 404 041 1 309 318 570 326
智能环境保护技术 8 382 10 423 11 139 14 994 ... 122 869 142 851 123 920 67 068
智能工业制造技术 4 163 4 767 4 723 6 149 ... 87 988 105 395 99 680 56 735
智能交通技术 3 557 4 391 4 804 6 950 ... 38 881 46 962 39 449 20 139
智能语音技术 212 262 248 316 ... 153 134 109 22
智慧储能技术 4 096 4 610 4 441 5 393 ... 1 011 44 138 502 149 700 81 013
智慧医疗技术 64 112 92 121 ... 287 267 219 103

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
p 0.007 990 234 0.001 244 539 0.000 769 667 -6.89E-03
q 0.253 028 989 0.364 428 988 0.409 428 917 0.450 611 78
m 5 056 468.594 13 384 332.64 1 465 927.09 834 305.769 2
p 0.001 584 822 0.000 172 421 -0.007 913 106 -0.016 082 792
q 0.393 864 018 0.499 672 553 0.473 161 046 0.642 808 038
m 503 535.602 4 6 488.885 569 1 058 656.364 7 004.486 878

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 2021 2022 2023 2024 2025
转发数 0.22 0.22 0.22 0.22 0.22
点赞数 0.16 0.18 0.16 0.18 0.16
评论数 0.18 0.18 0.18 0.20 0.18
情感得分 0.51 0.50 0.52 0.52 0.52

Table 6

Network structure under different thresholds"

年份 阈值 网络直径 平均聚类系数 边数/条
2021 0.6 4 0.355 8 193 363
0.7 6 0.374 6 177 752
0.8 12 0.404 7 108 227
0.9 13 0.228 7 6 991
2022 0.6 4 0.323 0 300 139
0.7 7 0.342 9 275 825
0.8 14 0.382 5 170 849
0.9 26 0.180 0 4 111
2023 0.6 4 0.366 1 150 885
0.7 5 0.385 4 137 409
0.8 9 0.417 8 74 518
0.9 17 0.141 8 1 372
2024 0.6 6 0.317 0 261 595
0.7 7 0.330 9 244 720
0.8 10 0.376 7 146 011
0.9 23 0.136 5 2 619
2025 0.6 5 0.413 3 91 897
0.7 7 0.431 3 83 942
0.8 9 0.444 9 34 591
0.9 15 0.111 0 599

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
BiLSTM 0.963 7 0.963 4 0.963 3 0.963 4
BiLSTM-Attention 0.961 5 0.962 2 0.960 1 0.961 0
TextCNN 0.971 9 0.971 9 0.971 3 0.971 6
RF 0.955 6 0.957 0 0.953 5 0.955 0
SVM 0.921 5 0.920 1 0.922 2 0.921 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 2021 2022 2023 2024 2025
CH指标 BERT-DTM 222.11 784.68 254.12 467.67 507.28
SBERT-DTM 538.82 292.24 189.37 904.45 189.08

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

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