Emotion is a complex physiological and psychological activity, accompanied by subjective physiological sensations and objective physiological changes. The body sensation map describes the changes in body sensation associated with emotion in a topographic manner, but it relies on subjective evaluations from participants. Physiological signals are a more reliable measure of emotion, but most research focuses on the central nervous system, neglecting the importance of the peripheral nervous system. In this study, a body surface potential mapping (BSPM) system was constructed, and an experiment was designed to induce emotions and obtain high-density body surface potential information under negative and non-negative emotions. Then, by constructing and analyzing the functional connectivity network of BSPs, the high-density electrophysiological characteristics are obtained and visualized as bodily emotion maps. The results showed that the functional connectivity network of BSPs under negative emotions had denser connections, and emotion maps based on local clustering coefficient (LCC) are consistent with BSMs under negative emotions. in addition, our features can classify negative and non-negative emotions with the highest classification accuracy of 80.77%. In conclusion, this study constructs an emotion map based on high-density BSPs, which offers a novel approach to psychophysiological computing.