Abstract:    Improving the structural resilience of innovation networks is an important theoretical and practical issue facing the high-quality development of the AI industry. This paper adopted the AI industry in Shanghai as an example, constructed a cooperative innovation network through text mining, and then used the ERGM to reveal the impact of endogenous factors of the innovation network on its structural resilience. The research found that: (1) The evolution of the structural resilience of the innovation network in Shanghai′s AI industry can be divided into three main stages: slow growth, rapid growth, and maintaining stability. The continuous improvement of network heterogeneity is the key driver for the transformation of the innovation network from an assortative core network to a resilient network; (2) The intermediary structure negatively affects the structural resilience of the artificial intelligence industry′s innovation network; (3) The expansive structure negatively affects the structural resilience of the AI industry′s innovation network in general, but the three-star structure positively affects its structural resilience. This study has addressed the gap in research on the resilience of innovation networks in the artificial intelligence industry, and it will help to deepen the theoretical understanding of innovation network resilience, thus having positive implications for enriching the data sources of innovation network research.

Key words: artificial intelligence industry, innovation network, structural resilience, exponential random graph model (ERGM)