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| Identification of Potentially Disruptive Technologies by Integrating Semantic and Citation Features |
| Qin Hao1,2, Zhao Yiming1,2,3,4, Ma Yakun5, Zhang Zhixin6, Wang Qigang7 |
1.Center for Studies of Information Resources, Wuhan University, Wuhan 430072
2.School of Information Management, Wuhan University, Wuhan 430072
3.Big Data Institute, Wuhan University, Wuhan 430072
4.Suzhou Institute of Wuhan University, Suzhou 215009
5.School of Information Management, Nanjing University, Nanjing 210023
6.School of Public Policy & Management, Tsinghua University, Beijing 100084
7.Shaanxi Nongjin Information Technology Service Co., Ltd., Xi’an 710000 |
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Abstract Disruptive technologies, as core drivers of industrial restructuring, play a critical role in shaping national technology strategies. To address the identification biases in existing research stemming from reliance on single-dimensional features, this study proposes a framework for identifying potentially disruptive technologies by integrating semantic and citation features. First, the PatentSBERTa model is employed to encode patent abstracts and extract semantic features, while a graph attention network is used to capture structural features from patent citation networks. Next, a dual-attention mechanism and a gated dynamic fusion module are designed and combined with a Bi-LSTM model to construct a supervised binary classification framework for identifying potentially disruptive technologies. The SHAP model is introduced to evaluate the contributions of semantic and citation features. Finally, an empirical study in the domain of carbon capture, utilization, and storage (CCUS) is conducted to demonstrate the application of the proposed framework, through which five potential disruptive technology directions and fifteen specific technology themes are identified. The results reveal that semantic features serve as the foundation for capturing the intrinsic innovativeness of technologies, whereas citation-based features provide critical external evidence for assessing their diffusion potential. Together, they form a complementary dual-verification mechanism of “intrinsic breakthrough-external diffusion.” In the context of innovation management under resource constraints, prioritizing technologies with intrinsic breakthroughs is recommended, followed by an assessment of their network niche based on citation features. This study offers a novel fusion analysis paradigm for identifying potentially disruptive technologies and provides actionable insights for national development policies and industrial planning in the CCUS domain.
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Received: 20 March 2025
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