This study explores the dynamics of emerging technology innovation ecosystems, viewing them as complex systems comprising social actors and knowledge artifacts engaged in innovation interactions. Employing a multilayer network perspective, we present a Social-Knowledge-Science-Technology (A-K-S-T) framework, examining both homogeneous and heterogeneous interactions among innovators and knowledge elements. Within this framework, we map out the technological landscape, identify ecological niches for specific actors and knowledge elements, and gauge knowledge proximity among innovators, revealing opportunities for collaboration and knowledge innovation. Using 5G technology as an illustrative example, key findings include the potential for innovation development in 5G, the need for enhanced collaboration among organizations in related technological fields, and the complementary nature of scientific and technological knowledge. This research contributes to innovation ecosystem literature, offering insights for management, governance, efficiency, and shared prosperity; meanwhile, it is a valuable reference for decision-makers to shape effective strategies.

Sustainable innovation in the field of artificial intelligence (AI) is essential for the development of Industry 4.0. Recognizing the innovation abilities of researchers is fundamental to achieving sustainable innovation within organizations. This study proposes a method for identifying the core innovative competency field of researchers through text mining, which involves the extraction of core competency tags, topic clustering, and calculating the relevance between researchers and topics. Using AI as a case study, the research identifies the core innovative competency field of researchers, uncovers opportunities for sustainable innovation, and highlights key innovators. This approach offers deeper insights for AI R&D activities, providing effective support for promoting sustainable innovation. Compared to traditional expertise identification methods, this approach provides a more in-depth and detailed portrayal of researchers’ expertise, particularly highlighting potential innovation domains with finer granularity. It is less influenced by subjective factors and can be conveniently applied to identify the core innovative competency field of researchers in any other research field, making it especially suitable for interdisciplinary areas. By offering a precise and comprehensive understanding of researchers’ capability fields, this method enhances the strategic planning and execution of innovative projects, ensuring that organizations can effectively leverage the expertise of their researchers to drive forward sustainable innovation.