Technological capability and innovation network resilience: evidence from the AI industry in China

In an era of escalating technological complexity, identifying core innovators is critical for mapping industrial trajectories and sustaining network resilience. Existing assessments predominantly rely on patent statistics and structural network centralities. However, these metrics inherently dilute substantive technological strengths and influence, thereby obscuring hidden core innovators in knowledge-intensive domains such as the Artificial Intelligence (AI) industry. To bridge this theoretical and methodological gap, this study develops a multidimensional, knowledge-driven evaluation framework that integrates text mining with complex network analysis. Leveraging 282,778 Chinese AI patents, this study deploys Latent Dirichlet Allocation to delineate fine-grained technological domains. Our work constructs a composite technological capability metric to identify core innovators and simulate targeted disruptions across collaboration and knowledge networks. The empirical results suggest that some innovators with substantial technological value are not necessarily located at the structural center of the network, indicating that network position alone may not fully capture the technological importance of innovators. Specifically, deliberate disruption simulations show that targeted attacks based on intrinsic technological capability led to a more pronounced decline in the knowledge network than attacks based on topological baselines. These findings suggest that substantive technological competencies play an important role in shaping network resilience and complement structure-based perspectives in understanding innovation networks.