Abstract: In the current greenhouse environment system, the environmental monitoring data can only reflect the current environmental conditions and cannot predict the changing trend of the greenhouse environment, resulting in poor greenhouse environment control effects. This paper proposed a greenhouse environment factor prediction method based on Elman neural network. With the collected historical data of temperature, humidity, and carbon dioxide concentration in the greenhouse as the input of the prediction model, an Elman neural network prediction model was established to realize accurate prediction of changes in greenhouse environmental factors. The results showed that the Elman model outperformed the BP and RBF models. The mean square errors of the temperature, humidity, and carbon dioxide concentration prediction results were 0.003 9, 0.005 9, and 0.028 3, respectively, and the coefficients of determination were 0.991 5, 0.967 8, and 0.973 9, respectively. The prediction results of this model were ideal, and it could provide certain decision support for greenhouse environment regulation.