In this study, air conditioning control software is designed with neural network technology, and the traditional manual control mode and neural network controller are compared. First, Energy Plus is used to build a real high-speed railway station building and its multi-connected air conditioning system, with 424 working conditions of the air conditioning system set up to complete the operation simulation for a whole year. Then the neural network controller is trained with data having excellent predicted mean vote (PMV)-based thermal comfort and energy consumption which are extracted from millions of simulation data. Finally, the prototype system of air conditioning control software for the high-speed railway station is developed with Java Enterprise Edition (JavaEE), and the dynamic control of air conditioners is realized by using Energy Plus simulation data and simulation with a machine learning prediction model. The simulation results based on this prototype software system show that the intelligent controller can reduce energy consumption in comparison with manual control based on fixed settings under typical working conditions in winter and summer.