Abstract: The temperature prediction of geothermal reservoirs at different depths is to determine the key parameters such as thermal energy storage, heat output capacity, and the sustainable utilization period of geothermal reservoirs. Taking the geothermal wells in the Qiabuqia area of Gonghe Basin as an example, this study proposes a temperature prediction model for heat reservoirs under different constraints based on particle swarm optimization (PSO) and long short-term memory network (LSTM). The prediction effect of this model is verified by comparing with those of the BP model and LSTM model. The results show that the RMSE value, MAPE value and MAD value in the prediction results of the model are the smallest compared with those in BP and LSTM models, and the minimum RMSE value is only 1.192. The determination coefficient of the model is 0.929, showing a good prediction effect. This indicates that this model could realize the prediction of reservoir temperature in geothermal system, which provides references for the efficient and long-term development of geothermal system.