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“People-oriented” and “Energy saving” are the two major themes of current social development. In the field of thermal comfort, establishment of thermal comfort model based on physiological parameters plays an important role in meeting the needs of human health and comfort, optimizing the design of building environment and building energy saving. In this paper, three types of physiological signals (skin temperature, skin conductance and heart rate) were collected through comfort physiological experiments. The changes of the three types of physiological signals under environmental temperature were analyzed. Furthermore, subjective questionnaire survey of human thermal comfort under five experimental conditions was performed. In addition, the thermal comfort evaluation model based on individual differences was established by partial least squares regression and ELM-RBF neural network. The established models were compared with the classical PMV model to analyze the superiority of the model. The results show that the thermal comfort evaluation model based on individual differences established by ELM-RBF neural network can better predict the trend of people's thermal comfort and satisfy the individual's demand for thermal comfort. Meanwhile, it can achieve the goal of building energy saving. Therefore, it has high practical and social significance.