关键词: 颠覆性技术, 技术融合, 技术生命周期, 链路预测, 机器学习

Abstract: Industry 4.0, represented by disruptive technologies such as virtual reality, cloud computing, and the Internet of Things, has led to the reshuffling of global traditional industries. It brings industrial transformation in various countries, reinvention and convergence of industries, forming a new industrial standards, industrial patterns and business models. The huge demand of national economic development for disruptive technologies makes it an important prerequisite to seize the initiative of innovation to accurately identify the field of disruptive technologies, accurately forecast the direction of disruptive technologies, and correctly guide the development of disruptive technologies. Based on the characteristics of cross-border convergence and diffusion of disruptive technologies, this paper proposes a dynamic prediction model architecture of disruptive technologies based on patent data from the perspective of technology convergence. Virtual reality, a representative disruptive technology, is selected and serves as the empirical object of this paper. The life cycle of this technology is creatively fitted by constructing a patent co-occurrence network and analyzing the cumulative number of patent co-occurrence, with 2010 identified as the dynamic starting point for the current prediction phase. To address data disequilibrium, a dynamic prediction model is developed using link similarity indices and machine learning classification algorithms. A comparison of various prediction models shows that the global similarity index outperforms the local similarity index in forecasting subversive technologies, and the random forest algorithm emerges as the most effective classifier. The prediction results suggest that the most promising areas for technological convergence with virtual reality include near-eye display devices, digital data processing technologies, and digital video transmission systems. By scientifically designing the prediction process for disruptive technologies, this paper not only effectively improves forecasting accuracy, but also provides theoretical foundation and methodological reference for identifying critical timing and selecting appropriate prediction models for the development of disruptive technologies.

Key words: disruptive technology, technology convergence, technology life cycle, link prediction, machine learning