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In network fault diagnosis, the present multi-class support vector machine (SVM) classification methods have low accuracy. The ensemble learning was introduced to improve the diagnosis accuracy. A new ensemble learning algorithm based on the disturbance of both binary tree structure and model (BTSMBag) was proposed. The diversity of individual classifiers was increased by the dual disturbance and the generalization of diagnosis model was improved. The experiment on the real network fault diagnosis shows the proposed method gets higher accuracy.