In order to explore the connectivity characteristics of the brain network of patients with depression and its feasibility as an online feedback indicator. First, the brain network is constructed using the imaginary part of coherency (IC) that is not sensitive to the volume conductor effect, this can effectively and conveniently avoid the influence of false connections. Then, the IC value with significant difference is extracted as a feature set, and a combination of Couple entropy (CE) and Relief filtering feature selection method is proposed to optimize the feature set, and the relationship information between features and classes, features and features are combined to improve the quality of feature sets. At the same time, according to the self-referencing brain network module integration feature set, online feedback indicators are constructed. Finally, K-nearest Neighbor (KNN) and support vector machine(SVM) classifiers are used for comparative analysis. The results found that the feature set extracted by the CE-Relief feature selection method in each frequency band is the smallest, and the classification accuracy is higher than 90%; the IC value of the Alpha frequency band has the best effect in identifying depression, and the classification accuracy can reach 100%; the classification ability of the average IC value of the prefrontal area of the self-reference brain network has advantages and stability in each frequency band, and the classification accuracy is higher than 80%.