Exploring the effective and efficient path of agricultural carbon emission reduction in Henan Province is of great significance to optimizing the strategic layout of China's agricultural emission reduction and carbon sequestration. Accordingly, we first scientifically measure the agricultural carbon emissions of each county and then utilize the standard deviation ellipse and spatial measurement model to scientifically analyze and clarify the spatial and temporal evolution trend and spatial effect mechanism of agricultural carbon emissions in Henan Province based on the county data of Henan Province from 2010 to 2020. The results show that: (1) In 2020, the total agricultural carbon emissions in Henan Province will be 134.7274 million tons, with the distribution of high in the southeast and low in the northwest, which has gradually become balanced, and the center of gravity is mainly concentrated in Xuchang county. (2) The spatial dependence of agricultural carbon emissions in Henan Province shows a four-stage trend of "fluctuating down ~ continuing up ~ plummeting ~ fluctuating up again", and the spatial heterogeneity is dominated by the "low-low" agglomeration, and the "high-low" agglomeration is dominated by the "low-low" agglomeration. The spatial heterogeneity is dominated by "low-low" agglomeration, followed by "high-low" agglomeration. (3) There is an "inverted U" curve relationship between the level of agricultural economic development and agricultural carbon emissions, the latter increases and then decreases as the former increases. The increase in the level of agricultural mechanization and urbanization rate will significantly reduce agricultural carbon emissions. The opposite is true for the financial support for agriculture, the income level of rural residents, and the structure of the agricultural industry. (4) In terms of spatial spillover effects, the increase in the level of agricultural development in neighboring counties will first increase and then decrease agricultural carbon emissions in this county. The mechanization level and urbanization rate of neighboring counties will reduce agricultural carbon emissions in this county, and vice versa for the income level of rural residents and the scale utilization of agricultural land.