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In the central heating of urban buildings, the “full open” and “fully closed” control modes at the end of heating not only have poor thermal comfort, but also cause large heat loss. For improving this problem, This paper proposes that the heating end flow control algorithm based on Q learning PID parameters is based on the study of reinforcement learning. First, we analyzed the thermal dynamics and heat transfer process of the radiator, and established a mathematical model of heat balance in the heating room. Then, the temperature deviation is the controller input, and the control valve opening degree control quantity is output based on the PID control algorithm. The temperature difference change is selected as the learning strategy of the intelligent body reward and punishment, and the PID parameters are adaptively adjusted online by the Q learning algorithm. Finally, we verified the controller"s regulation performance in the simulation experiment of central heating end flow regulation and compared with the traditional PID control results. The experimental results show that the adaptive PID flow control algorithm based on Q learning can make the indoor temperature change and the adjustment valve opening change more gradual, and save about 33% of the heat supply, and the energy saving effect is more obvious.