Taking greenhouse tomatoes as the research object, a forecasting method of transpiration of greenhouse tomatoes was proposed based on the real-time data of the Internet of things and random forest (RF) combined with gated recurrent unit (GRU) neural network. Firstly, the main factors affecting transpiration change collected by the sensor were preprocessed and RF was used to order the characteristic importance of the variables affecting the transpiration of tomato in greenhouse. Crop phenotypic parameters, including relative leaf area index, ecological parameters in greenhouse and cultivation environment parameters, including air temperature, relative humidity, light intensity, photosynthetically active radiation, substrate moisture content and substrate temperature were chosen as the input variables of the model. On this basis, a prediction model based on GRU was established to predict the transpiration of tomato. Finally, this model was compared with other models. At the same time, based on this model, a set of intelligent irrigation equipment was developed, which took the substrate water as the irrigation starting point and the predicted transpiration as the irrigation amount. The experimental results fully showed that the RF-GRU model had accurate prediction effect in tomato transpiration prediction and showed good feature learning ability in agricultural big data mining. The determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) were 0.9490, 10.96g and 5.80g, respectively. Compared with RF-LSTM and RF-RNN methods, the R2 was increased by 1.46% and 3.78%, the root mean square error was decreased by 1.38g and 3.24g, and the mean absolute error was decreased by 1.77g and 0.14g, respectively. At the same time, compared with regular irrigation, the intelligent irrigation system designed based on this model reduced the irrigation amount by 20% when the tomato growth was basically the same. This study could provide a reference for the research of greenhouse crop water requirements and it can be applied to water-saving greenhouse irrigation.