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.

In the digital era, technological catch-up is inevitable for firms confronted with intensified competition, rapid technological advancements, and customers’ upgrade requirements. By strengthening their cooperation with external parties and integrating internal knowledge, firms can better absorb internal and external resources, accelerating their technological catch-up performance (TCP). This study mainly explores the influence of collaboration and knowledge networks on the TCP of firms based on machine learning algorithms. First, patent data from the Chinese AI industry from 2013 to 2022 were used to construct collaboration and knowledge networks. Then, the hierarchical clustering algorithm was applied to categorize firms based on six network characteristics. Finally, the classification and regression trees (CART) algorithm was employed to analyze the nonlinear relationship between dual networks and firm TCP. The findings show that firms exhibit distinct network configurations and that the drivers of TCP vary across firm groups. For firms lagging behind, prioritizing knowledge network integration proves more effective than expanding collaborations. Leading firms perform best when maintaining balanced collaboration strategies. This study contributes to both theory and practice by identifying the optimal mix of network characteristics and providing empirically grounded strategies for different firm types.

Identifying the structural characteristics of a collaboration network that influence firms’ exploratory and exploitative innovation performance can help these firms enhance the output of innovation achievements and their core competitiveness. Based on 14,790 issued patents of 281 firms in the artificial intelligence industry in China, this study explores the complex nonlinear relationship between the structural characteristics of inter-organizational collaboration networks and firms’ exploratory and exploitative innovation performance by using clustering algorithms and classifications based on information entropy or the Gini index. The results indicate the following: (1) The four characteristics of degree centrality, closeness centrality, the local clustering coefficient, and structural holes affect the exploratory and exploitative innovation performance of firms. (2) In different firm clusters, there are different characteristic combinations that provide firms with various development strategies to improve this performance. (3) There are different paths that firms can take to improve this performance, which should be comprehensively considered along with the development goals of firms.

Enhancing green innovation performance is crucial for manufacturing enterprises to achieve sustainable development. This paper employs the strategic tripod framework (organization, industry, institution) using the K-means clustering algorithm to identify types of manufacturing performed by listed companies in China’s Shanghai and Shenzhen markets and adopts the CART decision tree algorithm to analyze influencing factors of green innovation performance across different enterprise types. The study finds that manufacturing enterprises can be divided into three types, with significant differences in influencing factors of green innovation performance. From the perspective of internal drivers, the improvement in green innovation performance mainly relies on organizational resource endowments, among which R&D ability is particularly key. From the perspective of the external institutional environment, the driving logic of mimetic pressure shows differentiated characteristics between different enterprise groups and differentiated response strategies need to be formulated accordingly. In addition, when the overall impact of external factors is weak, the level of industrial structure still has a prominent promoting effect on green innovation performance. Based on the data-driven perspective, this paper identifies the influencing factors of green innovation performance of different types of manufacturing enterprises, which is helpful to improve the green innovation performance of manufacturing enterprises.