Abstract: Improving agricultural green total factor productivity (AGTFP) is essential to the development of green agriculture. Few researches has evaluated the characteristics and influencing factors of the spatial correlation network of AGTFP, which is not conducive to the development of green agriculture. Therefore, based on relational data and networks, using data from 31 provinces (cities and autonomous regions) in China from 2010 to 2019, this study used the SBM-Undesirable model to determine AGTFP, and adopted the social network analysis method to analyze the overall structure and individual characteristics of the spatial correlation network of AGTFP. The dynamic process of factors affecting the spatial correlation network of AGTFP was analyzed through a quadratic assignment procedure (QAP) model. The results revealed that: First, the AGTFP in China showed an upward trend as a whole, and the average value increased from 0.47 in 2010 to 0.85 in 2019 with scope for improvement and high regional variability. In addition, the spatial correlation effect of AGTFP of provinces (cities and autonomous regions) exceeded geographical proximity, forming a complex spatial correlation network throughout the country. Second, the correlation and stability of the spatial correlation network of AGTFP were reinforced during the study period. From 2010 to 2019, the number of network relationships increased from 124 to 215, and the network density increased from 0.13 to 0.23. Meanwhile, the network level reduced from 0.53 to 0.29, and the network efficiency reduced from 0.84 to 0.67. Third, the centrality of the spatial correlation network of AGTFP in China fluctuated in different years. The eastern region, relying on a more developed economy, had become the main factor gathering place in the spatial correlation network; therefore, it had a high central degrees. While parts of the western region had a very high central degree due to the inflow of factors from the central and eastern regions mainly through policy support. A few areas in the central region haf a very high central degree due to their superior location. Finally, the results of this study demonstrated that the effects of influencing factors on the spatial correlation networks of AGTFP in China varied from year to year, and the level of economic development, agricultural development, informatization, transportation improvement, and spatial adjacency had a marked impact on the formation of the spatial correlation networks of AGTFP in China. Therefore, the characteristics and influencing factors of AGTFP should be considered, and effective measures should be taken to enhance the spatial correlation of AGTFP.