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Crop transpiration is one of the key parameters to guide crop irrigation. It is an effective way to save water to obtain crop transpiration in real time and realize irrigation on demand. However, the microclimate effect in greenhouse is significant, the relationship between crop transpiration and environmental factors is complex, and each environmental factor is interrelated and presents nonlinear change. Taking tomato as the research object, the weighing method was used to measure the real-time transpiration of crop. Greenhouse microclimate data could be obtained in real time through the installation of sensors, including air temperature (AT), relative humidity (RH) and light intensity (LI) as the microclimate environment input of the model, and canopy relative leaf area index (RLAI) as the crop growth input of the model. On this basis, a prediction model of tomato transpiration by long short term memory (LSTM) network was proposed. The model was used to predict the transpiration of tomato, and compared with the nonlinear autoregressive with exgeneous inputs (NARX) neural network, Elman neural network and recurrent neural network (RNN). The results showed that the determination coefficient (R2) and mean absolute error (MAE) of the LSTM prediction model were 0.9925 and 4.53g,respectively. Compared with NARX, Elman neural network and RNN, their R2 were increased by 8.97%, 1.18% and 0.82%, respectively, and their MAE were decreased by 8.16g, 6.23g and 0.52g, respectively. The prediction model proposed had high prediction accuracy and generalization performance, and the research results could provide reference for the study on the regularity and water demand of greenhouse crops.