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.

The importance of technology convergence of multidisciplinary knowledge has increased recently, and it is a crucial way to spur emerging technologies. Therefore, to understand and identify the technology convergence, which refers to the combination of two or more technological elements for a new system with new functions, is an important issue for both the researchers and the company directors. To identify and investigate the patterns of technology convergence, this research examines the numerical control machine tool, which has typical characteristics of technology convergence in recent years. Based on the numerical control machine tool related publications published between 1997 and 2019, we perform a deep learning approach based on Graph Neural Network model using publication citation network topology and text information together, to identify the technology convergence trajectory and to examine the dynamic role of corresponding technology sub-fields in the technology convergence. The results show that there was an obvious increase for the interdisciplinary citations from information technology to NC machine tool in recent years, and the technology convergence on NC machine tool is signal processing in machining and application of intelligent algorithms in motion control and process planning. In addition, the revelation of the technology convergence early identification contributes to the formation theory of emerging technologies that are interdisciplinary, and is of great interest to researchers, policy makers, and industrialists.

The combination of new-generation information technology and manufacturing technology has had in a significant and profound impact on the future development paradigm of manufacturing. Machine tools are the basis of virtually everything that is manufactured in the industry, exploring the development of the machine tool domain is of considerable significance to identify the opportunity to develop manufacturing industry and promote the sustainable development of manufacturing in the current tightening constraints of resource environment. Although much attention has been paid to development studies of a specific domain in recent years, it is challenging to conduct a multidimensional study related to the development status of the machine tool domain using existing methods. To solve this challenge, we propose an integrating framework combining topic models, bibliometric, trend analysis and patent analysis to mine multi-source literature within the machine tool domain, including papers, funds, patents, and news. Specifically, papers and funds provided two different perspectives to explore the development status in the research of machine tools. Furthermore, the technology development of machine tools was investigated through patents analysis. Finally, news related to the machine tool industry in recent years was analyzed to examine business focuses on machine tools. The integration of above various analytical methods and multi-dimensional mining of literature enabled analyzing the development of the machine tool domain systematically from multi-perspectives that include research, technology development and industry to provide inspirations about the implications of sustainable development of this domain. The conclusions obtained in this paper is beneficial to different communities of machine tools, in terms of determining the research directions for researchers, identifying industry opportunities for corporations and developing reasonable industry policy for policy makers.