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Abstract: Artificial intelligence (AI) deeply drives the quality improvement and efficiency enhancement of technology finance, while the deepening and expansion of technology finance also promote the iteration and application innovation of AI technology. Based on an analysis of the intrinsic coupling mechanism between AI and technology finance, and using provincial panel data of China from 2014 to 2023, this study comprehensively applies the coupling coordination degree model, the modified gravity model, and social network analysis to construct a spatial correlation network of coupling coordination. It further explores the evolutionary characteristics of the spatial correlation network in the coupling coordination relationship between AI and technology finance. The QAP regression model is employed to deeply analyze the driving factors of the spatial correlation network of coupling coordination. The findings reveal that: (1) The coupling coordination level between AI and technology finance development in China shows a trend of minor fluctuations, with significant regional gradient differences and dynamic evolution characteristics. The spatial correlation intensity exhibits a distribution pattern of “dense in the east and loose in the central and western regions.” (2) The spatial correlation network of coupling coordination displays stable and complex structural characteristics, with overall connectivity and stability continuously strengthening and the network structure gradually optimizing. From the perspective of individual network characteristics, provinces such as Beijing, Shanghai, and Jiangsu have long occupied core positions in the network, while most central and western provinces remain on the periphery. However, the network as a whole shows a flattening development trend, with connection paths becoming increasingly diversified. (3) The spatial correlation network can be divided into four plates: net beneficiary, broker, bidirectional spillover, and net spillover. Cross-plate associations dominate the interactions between these plates, with significant spillover effects observed. (4) QAP regression analysis indicates that geographical proximity, differences in innovation levels, disparities in technology market development, variations in economic development levels, and differences in urbanization levels are important factors driving the formation and evolution of the spatial correlation network. This research provides theoretical insights and policy implications for promoting the deep integration of AI and technology finance and achieving high-level coordinated development.

 

Keywords: Artificial Intelligence; Technology Finance; Coupling Coordination; Spatial Correlation Network; Social Network Analysis; QAP Regression Model