In this study, citrus seedlings were selected to estimate the predictions of evaporation. The air relative humidity and temperature were collected by sensors and mass method was used to collect the mass change of crops in real time as crop evaporation. The substrate relative humidity, temperature and EC value were used as environmental factors. With environmental factors as model input and crop evaporation as model output, a long short-term memory neural network(LSTM) prediction model was constructed. The optimized model structure and training parameters included 1 hidden layer of the LSTM model, 120 hidden layer nodes, 128 iteration samples, and 175 training iterations. The activation function of the network is tanh function, the learning rate was 0.001, and the time step was 72. The coefficient of determination(R2), root mean square error(RMSE) and mean absolute error(MAE) of LSTM prediction model were 0.993 9, 0.015 5 g and 0.011 3 g, respectively. Compared with the prediction effect of recurrent neural network(RNN) and gated cycle unit(GRU), the predicted evaporation value from LSTM prediction model was closer to the real evaporation value, and the relative error range of prediction results had the smallest fluctuation, RMSE and MAE were the smallest, and R2 was the largest, indicating that the prediction effect of LSTM prediction model was the best among these three models.