This paper proposes an analytic hierarchy model (AHM) to evaluate classification algorithms for credit risk analysis. The proposed AHM consists of three stages: data mining stage, multicriteria decision making stage, and secondary mining stage. For verification, 2 public-domain credit datasets, 10 classification algorithms, and 10 performance criteria are used to test the proposed AHM in the experimental study. The results demonstrate that the proposed AHM is an efficient tool to select classification algorithms in credit risk analysis, especially when different evaluation algorithms generate conflicting results.