Emotion recognition is closely related to many facets of our daily lives. However, it is difficult to achieve a satisfying emotion recognition rate by using one single algorithm. Therefore, this study puts forward an emotion recognition model based on electroencephalogram (EEG) with a fusion algorithm that combines the support vector machine (SVM) algorithm with the K-nearest neighbors algorithm (SVM-KNN). In the emotion classification process, the spatial distance between the sample to be identified and the optimal classification hyperplane is calculated. If it is longer than the preset threshold, the SVM classifier is chosen to classify the emotion records. Otherwise, the KNN classifier is chosen. Finally, experiments are carried out on the SJTU emotion EEG dataset (SEED). The comparative experiments show that the SVM-KNN algorithm improves the accuracy of the three-emotion classification. This model can effectively identify the types of emotions and thus has positive significance in obtaining the emotions of patients with expression disorders in medical care.