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The environment temperature of passenger railway station is easily affected by other environmental characteristic variables such as humidity, PM2.5, carbon dioxide, etc. The traditional univariate prediction algorithm does not consider the influence factors of other environmental characteristic variables.In order to further accurately predict the environmental temperature of the passenger station, a combined model combining LSTM neural network and LightGBM gradient lifting algorithm is proposed to predict the environmental temperature of the passenger station. Firstly, the pre-processed data were input into the LSTM model, and environmental characteristic variables such as humidity, carbon dioxide, PM2.5 and PM10 were predicted. Then input the predicted value of environmental characteristic variables output by LSTM into LightGBM model to get the predicted value of temperature. According to the comparison and analysis of the waveforms and RMSE, the combined model prediction based on LSTM-LightGBM can retain the periodicity of the univariate prediction used by LSTM model, and can show the non-stationary changes of the temperature prediction after the environmental characteristic variables input into LightGBM model. The results show that the combined model method based on LSTM-LightGBM is closer to the original waveform and has lower RMSE than the method using LSTM alone.