The accurate prediction of greenhouse environment variation based on the constructed prediction model is helpful to precisely regulate the crop environment, and promote the growth of fruits and vegetables. Due to the coexistence of multiple parameters, complex coupling with each other, temporality and nonlinearity of greenhouse microclimate environment, the accurate prediction model is difficult to establish. Based on above issues, a greenhouse environment prediction model was proposed based on the sparrow search algorithm (SSA) optimized-long short term memory (LSTM) neural network method, so as to realize the prediction of greenhouse environment data sequence with the Internet of things (IoT) collecting accurate multipoint environment data. The experimental results showed that the automatic parametric optimization process by SSA could deal with the time consuming problem of manual parameter selection for the LSTM model. The proposed SSA-LSTM method could lower the model training time, and the optimal parameters selection could make sure the model worked with the optimum capability. The trained SSA-LSTM model was used to predict six kinds of greenhouse environment data, including the air temperature, air humidity, soil temperature, soil humidity, CO2 concentration, and the illumination intensity. The proposed SSA-LSTM could realize a 97.6% average prediction fit index, compared with the back-propagation network, the gated recurrent unit neural network and the LSTM, the prediction fit index was elevated by 8.1 percentage points, 4.1 percentage points and 4.3 percentage points. Therefore, the prediction accuracy of SSA-LSTM was obviously improved. The research result could provide reference for the development of greenhouse environment control strategy and deal with the lag problem of environment control.