Specialized, Refined, Differentiated, and Innovative (SRDI) enterprises are crucial to China’s economic development. It is important to examine how various factors’ combinations impact the radical innovation performance of SRDI enterprises in order to promote high-quality regional economic development. Based on the Technology–Organization–Environment (TOE) framework, this study selected SRDI enterprises as research samples, used a hierarchical clustering algorithm to divide the enterprises into groups according to the characteristics of SRDI enterprises, and employed a classification and regression tree (CART) algorithm to reveal the complex nonlinear relationships between the combinations of multiple key influencing factors and radical innovation performance from multi-source big data. The findings indicate that (1) there are significant variations in the factors affecting the radical innovation performance of different types of SRDI enterprises; (2) the radical innovation performance of SRDI enterprises stems from the synergistic interaction among various factors; and (3) the impact of R&D investment on radical innovation is not simply linear. This study effectively captures the complex nonlinear relationships between combinations of multiple influencing factors and radical innovation performance. It is of great practical significance for revealing SRDI enterprises’ radical innovation performance improvement pathways and enhancing their innovation capability.

Based on authorized patents of China’s artificial intelligence industry from 2013 to 2022, this paper constructs an Industry–University–Research institution (IUR) collaboration network and an Inter-Firm (IF) collaboration network and used the entropy weight method to take both the quantity and quality of patents into account to calculate the innovation performance of firms. Through the hierarchical clustering algorithm and classification and regression trees (CART) algorithm, in-depth analysis has been conducted on the intricate non-linear influence mechanisms between multiple variables and a firm’s innovation performance. The findings indicate the following: (1) Based on the network centrality (NC), structural hole (SH), collaboration breadth (CB), and collaboration depth (CD) of both IUR and IF collaboration networks, two types of focal firms are identified. (2) For different types of focal firms, the combinations of network characteristics affecting their innovation performance are various. (3) In the IUR collaboration network, focal firms with a wide range of heterogeneous collaborative partners can obtain high innovation performance. However, focal firms in the IF collaboration network can achieve the same aim by maintaining deep collaboration with other focal firms. This paper not only helps firms make scientific decisions for development but also provides valuable suggestions for government policymakers.