Since the semi-closed and semi-opened spaces such as high-speed railway station have the indoor thermal comfort level difficult to predict, a Deep Forest (DF)-based deep learning method was proposed to realize the scientific prediction of thermal comfort level. Firstly, the heat exchange environment of high-speed railway station was modeled based on field survey and Energy Plus platform. Secondly, 8 influence factors, such as passenger density, operating number of multi-evaporator air conditioners and setting temperatures of multi-evaporator air conditioners, were presented, and 424 operating conditions were designed to obtain massive data. Finally, DF was used to obtain the relationship between thermal comfort and influence factors in order to predict the indoor thermal comfort level of high-speed rail station. Deep Neural Network (DNN) and Support Vector Machine (SVM) were provided as comparison algorithms for the verification. Experimental results show that, among the three models, DF performs best in terms of the prediction accuracy and weighted- F ₁, and has the best prediction accuracy of 99.76% and the worst of 98.11%. Therefore, DF can effectively predict the indoor thermal comfort level of high-speed railway stations.