fMRI applications in emotion recognition can assist doctors in better assessing patients’ emotions, thereby aiding in the development of personalized treatment plans. This study incorporating an SE block into the first layer of the CNN model to automatically focus on brain regions related to emotion classification among 246 fMRI signals (SE method). The results for the Class classification task achieved a Precision, Sensitivity, and F1-score of 0.73, 0.73, and 0.73, respectively, and for the Level task, they were 0.23, 0.32, and 0.26, respectively. Additionally, in this study, directly using brain regions previously identified as emotion-related as model input resulted in Precision, Sensitivity, and F1-scores of 0.49, 0.48, and 0.47, respectively, and for the Level task, scores of 0.04, 0.07, and 0.05 were achieved. The results indicate that employing the SE method to focus the model on brain regions associated with emotions enhances performance in emotion recognition tasks, achieving an error rate of 0.4213 in the second phase of the ICBHI challenge.