Multi-domain Knowledge Convergence Trajectory Analysis of Strategic Emerging Industries Based on Citation Network and Text Information

Abstract

The analysis of technology convergence process for strategic emerging industries is helpful to deeply understand the generation process and development law of industrial technology, thereby helping master the development trend of the field and promoting the healthy development of the industry. To identify the trajectory and degree of technology convergence of the strategic emerging industries, this study conducts a multi-case study on four fields which present a trend of convergence and attract social attention, namely, high-end equipment manufacturing, new-generation information technology, new medicine, and new energy. This study adopts a knowledge convergence trajectory analysis method based on citation network and text information. It utilizes a graph neural network model and encodes the citation network, title, and abstract of the publications as vectors. Five knowledge convergence trajectories are identified, after analyzing the data of the selected four technical fields. The research results show that information technology and numerical control equipment, biomedicine and solar photovoltaic technology have shown a trend of deep convergence, respectively; and the convergence of the information technology and numerical control equipment is deeper. Numerical control equipment and solar photovoltaic technology, information technology and solar photovoltaic technology have shown a converging trend, respectively; however, the current degree of convergence is still insufficient, due to the late start of convergence. Numerical control equipment and biomedicine have not shown any trend of convergence.