【Purposes】 Studies of brain networks in schizophrenia (SCZ) have shown that both structural and functional networks are altered in patients. Extracting accurate discriminative features from brain networks as classification features can improve the classification accuracy of SCZ and avoid the deficiencies caused by subjective diagnosis relying on scales. Traditional brain network features such as betweenness centrality and clustering coefficients lose topological information, and minimum spanning tree loses some brain region connections. Although subgraphs retain topological information, the screening of traditional discriminative subgraphs generates some redundant information, which in turn affects the classification accuracy. 【Methods】 In this paper, a screening method for discriminant subgraphs based on frequency ranking (Frequency Scoring Screen, FSS) is propsed. FSS screens discriminative subgraphs and eliminates redundant information without losing the original discriminative information. The classification performance of using different features and different classification algorithms is compared by using publicly available data from OpenfMRI. 【Findings】 The results show that the classification performance of the FSS feature is better than that of other traditional brain network features, the feature is not affected by the classification algorithm, and the classification accuracy is better than existing SCZ classification literatures.