At present, the clustering method based on similarity is used to classify the wind power output scene, and the similarity is mostly measured by the Euclidean distance. Hence, the results reflect the difference of the amplitude of the time series curve, not the difference of the morphological characteristics and changing trend of the curve. This study proposes a method of wind power output scene division based on Gaussian mixture clustering, that is, the final attribution category is judged by the probability of belonging to a certain category. Firstly, the optimal numbers of GMM clustering and K-means clustering are determined according to BIC criterion, elbow rule and contour coefficient, respectively. Then, taking the actual wind power in a certain area as the research object, the typical scenes of wind power output in spring in this area are extracted, and the two clustering results are compared and analyzed to verify the effectiveness of this method. Finally, the typical scenes of wind power output in each season in this region are extracted by GMM clustering model.