Aiming at the target person’s emotional changes, this study proposes a method of emotion prediction to identify, predict, and analyze emotions. Before sentiment prediction, a sentiment quantitative algorithm is used to normalize the sentiment data set to obtain the degree coefficient corresponding to each sentiment, which lays the foundation for the next sentiment prediction. Then, we summarize the mood changes of the target person for one day to get a main mood, and then use the mood prediction algorithm to get the final prediction result. In this study, Bidirectional Encoder Representations from Transformers (BERT) neural network is used to model the emotion of short dialogues in order to achieve real-time emotion prediction of target person. The results show that the application of the training model in this study can effectively determine the future mood fluctuations of the target person.