At present, the traditional control strategy of heating systems is still unable to achieve building heating on demand, which enhances the energy consumption of heating and affects the thermal comfort of buildings. Therefore, this study puts forward a novel data-driven MPC for building thermal inlet, which allows the optimal operation of the district heating system and has been verified by simulation with three public buildings. In this method, the indoor temperature at the next moment reaches the temperature set value by changing the current flow rate. First, based on the energy consumption monitoring platform and the measured data of the buildings, the building indoor temperature prediction model at the next moment is established by using long short-term memory (LSTM). Compared with subspace model identification (SMI), LSTM has higher prediction accuracy, and the R2 was about 0.9 in three buildings. Second, the particle generated by particle swarm optimization, which represents the flow variation, is input to the trained LSTM to predict the indoor temperature. By minimizing the objective function, the optimal flow change at the current time can be calculated. The results showed that the MPC based on a data-driven model can adjust the flow rate in time to maintain a stable indoor temperature with ±0.5 °C error. In addition, when the temperature setting needs to be changed, the indoor temperature can reach the new set value in 3 h, which outperforms the PID control. The method proposed in this paper can greatly reduce the influence of regulation lag by adjusting the flow in advance.