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After thoroughly analyzing the relationships between indications and faults, it has been found that there are no explicit mapping functions between the faults of oil-immersed power transformer. To handle this problem, a multilevel decision-making model for power transformer fault diagnosis based on statistical learning theory is presented. Based on the concentration distribution of some typical fault gases, the proposed approach is to determine the optimal solution with a few training samples. The output of this model is improved by approaching exactly with K-nearest neighbor search classification for the SVM classification results, which is adjacent to optimal separating hyperplan. So the dependability of this model is enhanced greatly, and its effectiveness and usefulness is proved.