Agricultural green development is an essential pathway to achieving comprehensive agricultural and rural modernization and holds significant importance for ensuring national food, resource, and ecological security. Based on panel data from 30 provinces in China during 2004–2022, this study employed the super-efficiency SBM-GML model, the modified gravity model, social network analysis (SNA), and the quadratic assignment procedure (QAP) regression model to systematically analyze the spatial association network characteristics and driving mechanisms of agricultural green development in China. The results showed that (1) the number of spatial linkages in interprovincial agricultural green development had been increasing, with the network exhibiting strong connectivity, stability, and accessibility. (2) Major grain-producing areas and economically developed regions along the eastern coast had become the driving sources of spatial spillovers in agricultural green development. Meanwhile, the central and western regions acted as “brokers” in facilitating the reception and transfer of resources within the overall network, while municipalities such as Tianjin and Shanghai exhibited siphon effects on other regions. (3) Geographical proximity, government fiscal support, rural labor force size, progress in green technologies, and the agricultural economic development level significantly enhanced the spatial spillover effects of agricultural green development. However, regional disparities in agricultural industrial structures served as a key obstacle to realizing these spillover effects.

In this study, the transcendental logarithmic cost function model was used to measure the rate of technological progress in seven major mandarin-producing regions and seven major tangerine-producing regions in China from 2006 to 2021. The modified gravity model was used to establish spatial correlation networks. The social network analysis method was used to analyze the characteristics of the overall network structure and the individual network structure of the spatial correlation networks of citrus-production technology progress, and the quadratic assignment procedure was used to analyze the factors influencing the spatial network. The results show the production of Chinese mandarins and tangerines is in the stage of technological progress in general, but the rate of progress is slowing down gradually, and the rate of mandarin-production technology progress is higher than that of tangerine-production technology progress. In terms of the overall network structure characteristics, the spatial networks of technological progress related to Chinese mandarin and tangerine production are becoming increasingly dense and complex, with obvious spatial spillover effects, but the network structure is relatively loose, and the polarization of the tangerine network is more serious. In terms of individual network structure characteristics, the relatively economically developed eastern regions have a higher status in terms of the spatial correlation network and a stronger role in controlling and dominating the resource elements needed for citrus-production technology progress. Education, informatization, economic development, innovation support, and financial support are important factors influencing the formation of the spatial association network of citrus-production technology progress in China.