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| Construction and Empirical Investigation of Early Identification Methods for Disruptive Technologies Based on Weak Signal Perception |
| Wang Zhengyuan1,2,3, Jin Junbao1,2,3, Cao Kun1,2,3, Zheng Yurong1,2, Bai Guangzu1,2,3, Cai Xin1,2,3 |
1.Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000
2.Key Laboratory of Knowledge Computing and Intelligent Decision of Gansu Province, Lanzhou 730000
3.Department of Information Resources Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 |
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Abstract As the new era of technological revolution and industrial transformation gathers momentum, the early identification of disruptive technologies has become critical for nations to seize strategic high ground and reshape industrial competition landscapes. This study focused on the key segments of the scientific innovation chain (frontier research-basic research-applied research). By constructing thematic citation networks across these segments based on inter-document citation relationships, it tracked changes in triplet structures within the network over consecutive periods to identify weak signals of potential disruptive technologies. It then established a monitoring indicator system for early-stage disruptive technology signals, calculated the disruptive potential index for seed technologies, and incorporated large language models for semantic enhancement and noise filtering. This approach enabled precise identification and screening of early-stage disruptive signals within specific domains. This framework was applied to the hydrogen fuel cell field, identifying five major disruptive research directions including electrolyte and membrane technologies. The method was validated through a retrospective literature analysis, enabling the dynamic monitoring of disruptive weak signals across the entire chain from fundamental to applied research. The results demonstrate that the proposed framework significantly enhances weak signal resolution capabilities, identifying more disruptive technology directions with potential societal impact.
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Received: 22 September 2025
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