To accurately forecast the volume of agricultural carbon emissions in China, this paper proposes a forecasting model that combines the Autoregressive Integrated Moving Average Model (ARIMA) with Long Short-Term Memory networks (LSTM) to better handle both linear and non-linear parts in time series. Using the data on China's agricultural carbon emissions collected from 2000 to 2023 as the research subject, the data from 2000 to 2016 were first used as the training set, and the data from 2017 to 2023 were used as the test set. The Spearman's rank correlation coefficient method was utilized to study the correlation between various types of carbon emissions. Then, the established ARIMA-LSTM forecasting model was applied to predict the emissions in the test samples and make comparisons. The Absolute Relative Error (ARE) was adopted as the evaluation metric for different models, comparing the prediction errors of the ARIMA-LSTM model with those of the ARIMA and LSTM single models through analysis. The results show that the positive correlation between diesel carbon emissions and film carbon emissions was the highest; the absolute relative error of the ARIMA-LSTM model did not exceed 7%, which is significantly lower than the other two comparison models. This research model achieves high-accuracy predictions of carbon emissions, which is beneficial for the control and management of the greenhouse effect.