There is an important application value for the research of vehicle active safety technology through the recognition of drivers’ emotional state. In this study, seventeen subjects’ frontal dual-channel EEG signals were collected by emotional video induction method, and EEG characteristics of different emotions were extracted. After dimensionality reduction, the data were classified by multiple classifiers. The results show that compared with single-core classifier and ensemble learning classifier, Gradient Boosting Decision Tree (GBDT) algorithm has the highest recognition accuracy of happiness and sadness. This study provides a new method for real-time monitoring and recognition of drivers’ emotional state, and provides a theoretical guarantee for improving driving safety.