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| Identification of Innovation Opportunities by Coupling Dimensions and Laws Under the Topics Evaluation and Screening of Technological Prediction Network: Based on the Triple Complex Relationships Between Technological Knowledge Elements |
| Wang Jinfeng1,2, Wang Congxiang2, Zhang Ke3,4,5, Feng Lijie6, Feng Yicheng7, Wu Zhishuang2 |
1.China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306
2.School of Management, Zhengzhou University, Zhengzhou 450001
3.School of Information Management, Zhengzhou University, Zhengzhou 450001
4.Research Center for “Double World-Class Project” of Henan Province, Zhengzhou 450001
5.Data Governance Research Center of Henan Province, Zhengzhou 450001
6.School of Logistics Engineering, Shanghai Maritime University, Shanghai 201306
7.School of Advanced Manufacturing and Robotics, Peking University, Beijing 100871 |
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Abstract Exploring the complex relationships between technological knowledge elements for technological innovation not only allows enterprises to accurately and efficiently predict the development trend of related technologies by integrating opportunities between different technological knowledge elements but also allows enterprises to quickly respond to market demand and plan in advance to achieve sustainable development. However, existing research on identifying opportunities for technological innovation lacks sufficient exploration and integration of the complex relationships between technological knowledge elements and relies heavily on upstream task results combined with domain knowledge. This results in a certain level of blindness and disorder in technological innovation, significantly reducing the efficiency and quality of technological research and invention. In response, this study proposes an identification path for dimension-law coupling innovation opportunities through topic evaluation and screening of technological prediction networks using triple complex relationship mining between technological knowledge elements. First, by mining the direct semantic co-occurrence relationship, coupled co-occurrence relationship, and citation relationship between technological knowledge elements and using a weight optimization algorithm, a technological prediction network integrating the aforementioned triple complex relationships was constructed. Second, a three-dimensional strategic coordinate model was used to evaluate and screen out the core immature popular topics in the technological prediction network. Subsequently, based on the extraction of core immature popular topics subnets, an element variation innovation theory was used to identify technological innovation opportunities from the perspective of dimension-law coupling. Finally, a case study was conducted using 3D printing technology as an example to demonstrate the feasibility of the method proposed in this study. This study aims to provide reference concepts for accurately predicting the correlations between technological knowledge elements and efficiently identifying opportunities for technological innovation.
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Received: 07 April 2025
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