Thermal comfort is a subjective perception, hence conventional evaluation using meteorological factors faces a technical challenge in precise assessment. Human beings have the nature to differentiate expressions of facial emotions when varied thermal environments are perceived. Facial expression scores can be taken as a predictor of perceived thermal comfort which can be precisely assessed using deep learning against physical factors. In this study, a total of 8314 facial photos were obtained from volunteers in 82 parks of 49 cities via social network. Facial expressions were analyzed to happy, sad, and neutral emotion scores using a professional instrument. Temperature-responsive changes in sadness score (SS) can be fit by a U-shaped curve which was called as the 'sadness smile'. The stationary point of second-order derivative was identified to predict the-most-comfort temperature (22.84 °C), across which a tangent line framed the range of comfort temperatures based on two intersections with first-order derivatives (14.62-31.06 °C). Critical temperature points were identified along a positively correlated line of modified temperature-humidity index against increasing temperatures, which were negatively correlated with SS in autumn and winter. The ResNet model was demonstrated to excellently predict emotion-based thermal comfort perceptions in validation set (R