Enterprise credit risk evaluation is of great importance in the credit process in the commercial banking system. Based on the previous researches on the commercial bank credit risk assessment model, this essay starts from analyzing the factors which will influence the commercial bank credit management, and then moves on to build a more comprehensive credit risk evaluation index system which contains 3 levels of 12 indexes. Afterwards, this essay chooses the GRNN Neural Network to be the commercial bank credit risk assessment model by means of MATLAB. All the chosen samples have been put into empirical analysis and the results shows that the discriminate accuracy rate for the good enterprises is 92.16% while for the bad enterprises is 93.75% in this model. Therefore, this outstanding discriminate accuracy rate proves that this model could be used effectively and efficiently.