Due to the important role that credit risk evaluation (CRE) plays in banks, it is crucial that we improve the accuracy of the CRE model. In this paper, we employ a hybrid approach to design a practical and effective CRE model based on a deep belief network (DBN) and the K-means method. First, using the K-means method, we preprocess the original data set and reclassify the unrepresentative data into high grades. Second, based on the results of our K-means method analysis, a DBN is used to construct a CRE model. We randomly select data in fixed proportions from the samples that have been assigned to new classes in order to train and test the DBN model. Finally, German and Australian credit data sets are applied to the proposed model. Our results show that the hybrid classifier we propose is effective in CRE and performs significantly better than classical CRE models.