关键词: 潜在颠覆性技术, 市场特征, 层次化, 多维市场需求, 主题识别, 演化分析

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