Abstract: Maritime fire incidents pose a significant threat to the safety of ships,with human factors being the primary cause of these accidents. Accurately identifying the emotional changes of crew members in maritime fire scenarios is of great significance for enhancing their firefighting capabilities. Virtual reality technology is employed to simulate maritime fire scenes and collect Electroencephalogram（ EEG） signals from multiple subjects. The EEG signals are preprocessed and decomposed into sub-signals of different frequency bands using discrete wavelet transform. Three features,including mean absolute value,standard deviation,and root mean square,are extracted from each sub-frequency band to establish a feature set. Multiple machine learning models suitable for emotion recognition are constructed,and the models are evaluated using metrics such as precision,accuracy,and F₁ score. Experimental results show that the support vector machine classification model performs the best,with an accuracy of 87. 97%,which significantly improves the three-class classification problem of crew members’ fear emotions in maritime environments. Combining virtual reality technology with EEG emotion recognition techniques can effectively induce and identify crew members’ fear emotions in fire scenarios. This method is beneficial for assessing and improving the emergency response capabilities of crew members in firefighting training.