As many factors affect the thermal comfort of semi-enclosed buildings such as high-speed railway stations in a complex way and there exists contradiction between thermal comfort and energy consumption, a comprehensive prediction method for thermal comfort and energy consumption of high-speed railway station based on machine learning was proposed. Firstly, with sensor data capturing and Energy Plus platform, the indoor and outdoor status, the control units like multi-evaporator air conditioners and heat exchangers as well as the thermal energy transmission environment of high-speed railway station were modeled. Secondly, eight factors influencing the thermal comfort of high-speed railway station, such as the operating number of multi-evaporator air conditioners and setting temperatures of multi-evaporator air conditioners, the operating number of heat exchangers, passenger density, outdoor temperature, indoor temperature, indoor humidity, and indoor carbon dioxide concentration, were proposed, 424 model operating conditions and 3 714 240 instances were designed. Finally, in order to effectively predict indoor thermal comfort and energy consumption of high-speed railway station, six machine learning methods, which are deep neural network, support vector regression, decision tree regression, linear regression, ridge regression and Bayesian ridge regression, were designed. Experimental results show that decision tree regression has the best prediction performance in a short time with average mean squared error of 0.002 2. The obtained research results can directly provide actively predicted environmental parameters and realize real-time decision-making for the temperature control strategy in the next stage.