Third-person pain is an interesting empathy phenomenon that human has the ability to infer characters of other sufferers’ pain by observing their behavior. In the literature, existing studies suggesting that first-person and third-person pain share common features of neuroimage, which indicates that pain behavior will cause influence on both sufferer and the observer. Consequently, it is significant to explore the third-person effects upon the observer. In this study, the evaluation and recognition of third-person pain experience was studied based on user physiological signal analysis. We built a third-person pain multimodal physiological features dataset and applied machine learning methods to explore a third-person pain experience recognition model. A classification accuracy of 95.83% was obtained in third-person pain degree recognition, which demonstrates the effectiveness of our approach. The proposed study shed light on the guiding future exploration of determinants of third-person pain process and empathy intelligence.