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The automatic control method of indoor thermal environment based on thermal sensation prediction provides a new approach to solve the problem that the control based on traditional temperature set point does not meet the occupant comfort. However, it is a difficult task to acquire the thermal sensation of occupant during the modeling process. Therefore, a convenient intelligent interactive system of mobile terminal is developed to detect real-time field data to further establish learning samples. In addition, according to the dynamic variability feature of thermal comfort, the dynamic evolving neuro-fuzzy inference system (DENFIS) is designed so that the on-line forecasting model of occupant’s thermal comfort is established. Base on the data-driven of real-time learning samples, the system"s fuzzy rules and the coefficients of model output function can be dynamically self-corrected to predict the occupants’ preference temperature further. The result of experiment shows that the designed DENFIS algorithm predicts the occupant"s comfort temperature range as high as 90.5%, and the error was very small. It is proved that the on-line forecasting model established with the approach can be served as an intelligent air-conditioning temperature controller to solve the problem of unreasonable temperature set caused by the existing temperature set-point method, which is feasible in practical application.