Load forecasting is a key part of system optimization control of air conditioning system, the randomness and uncertainty of personnel movement make it difficult to accurately estimate the load of building space personnel, resulting in poor control effect of existing control strategies, the slow response of the system, the waste of energy and the reduction of thermal comfort of the building interior environment. Therefore, an air conditioning terminal and new air volume classification control strategy based on crowd density estimation is proposed. First, the image information of building space was collected, a multi-column convolution neural network crowd density estimation model was established to obtain the number and dynamic distribution of personnel and calculate the real-time load of building space personnel. Secondly, introduce the personnel load control factor and put forward the air conditioning grading control strategy to realize the air conditioning terminal and fresh air supply. The experimental results show that the method in this paper can better maintain the internal environment stability of the building, at the same time, the system has faster response speed and greater energy saving potential.