As technology convergence among strategic emerging industries deepens, the boundaries among technologies are increasingly blurred and redefined. Understanding the interconnections between technologies and grasping the structural dynamics and innovative trends within technology clusters from a global perspective are of significant value for efficiently allocating technological resources and enhancing innovation output. Currently, the studies related to technology convergence and innovation situation assessment mostly use small-sample patent data from specific industries or specific regions, which may lead to biased results. However, due to the difficulty of data acquisition and analysis, few scholars have conducted relevant research on technology integration and innovation situation assessment based on global patent big data. The importance of relevant research data in supporting the formulation of macro-innovation guidance policies is self-evident.
Therefore, this study utilizes global PCT (Patent Cooperation Treaty) big data to develop models for identifying key core technology clusters and assessing innovation trends. It first collects 4 611 149 PCT applications, 14 887 452 records of IPC categories, and 23 961 143 PCT citations.The RIT index is constructed to classify all technology domains into four technology categories, and this paper unfolds along two logical lines: "patent big data + technology convergence + technology clusters" and "PCT patents + RIT index + h-strength index." In details, IPC co-occurrence relationships of all domains are extracted to construct a global technology convergence network map; the core structure of the network map is extracted by using h-strength; and the results are carefully interpreted by combining the algorithmic identification of the network communities with the interpretation of technical experts. A total of 2 mega, 8 large, 30 medium and 34 smaller-sized key technology communities are identified in this study for global and domain-wide.
This research framework aims to provide data support for understanding global technological innovation hotspots and assisting in the formulation of macro-industrial innovation policies. In terms of theoretical innovation, this paper integrates three research topics: innovation measurement, technology convergence, and technology clusters. For instance, in the analysis of IPC co-occurrence network graphs, the paper combines the classification of key technology innovation trends with the analysis of the characteristics of key technology convergence clusters.
According to a new indicator, the relative impact of technology (RIT), its two-dimensional scatter chart and IPC co-occurrence core network, 76 core technology integration communities around the world and in all fields are identified, and their innovation trends are evaluated. The study finds that pharmaceuticals and the transmission of digital information and wireless communication networks are the main driving forces for global technological innovation. Optical elements, systems, and apparatus show strong characteristics on the whole, and semiconductor devices, new energy vehicles and autonomous driving present four different innovation situations, namely strong, emerging, recessionary and sleeping characteristics, which present obvious technological iteration. In addition, technical fields such as medium-scale catalysts, layered products, additive manufacturing, copolymers and copolymerization processes, semipermeable membranes and preparation, and surface liquid coatings also show distinctive characteristics.
In the conclusion section, the paper emphasizes technical interpretations, along with policy recommendations and future research prospects. In subsequent studies, it is suggested to systematically compare China's technological development context with international mainstream trends based on current technology community divisions. This would help clarify China's structural characteristics and directions of innovation, analyze China's technological advantages and disparities, identify potential technological development risks, and propose innovative governance strategies for China's cutting-edge technologies. Moreover, the paper provides policy suggestions for enhancing China's discourse power in global innovation strength evaluation and integrating into the global innovation ecosystem. Overall, this study contributes to a comprehensive understanding of global technology convergence and innovation dynamics, offering valuable insights for policymakers and researchers alike.As for the research deficiencies: on the one hand, h-strength is used to intercept the core network mapping of co-occurring relationships among IPCs, which makes isolated and smaller technologies excluded from the core network; on the other hand, the data sources and analysis indicators of this study are more limited, mainly relying on PCT patents, IPC classification numbers and citation indicators. Future research could incorporate more diverse data sources and analytical indicators, such as tripartite patent datasets, natural language processing models, and non-patent citations.