In view of the sparse reward problem in the training of energy consumption control systems using reinforcement learning methods, a deep deterministic policy gradient (DDPG) method based on the self-supervised network is applied to the building energy consumption control. First, the processing state and action variables are regarded as the input of the self-supervised network forward model, predicting the feature vector of the next state and using the prediction error as the internal reward of curiosity to solve the sparse reward problem. Then, a data-driven method is used to train the building energy consumption model with weather data as input and energy consumption data as output. Finally, the DDPG method based on the self-supervised network is used to develop the optimal control strategy, and the optimal discharge temperature of the air handling unit (AHU) is set based on the strategy to reduce the energy consumption of the equipment. Experimental results show that this method can achieve good energy-saving effects on the basis of maintaining a comfortable building environment.