In this paper,Adaboost algorithm is applied in credit ratings firstly,and empirical analysis shows that Adaboost algorithm on the basis of 18 indexes selected as regression variables fits the credit rates of 39 listed iron and steel companies of China very well.The discrimination errors are 2.56% after 10 iterations,given iterations added then its errors could reach zero and output classification results stably.In additions,the index importance outputs used from two aspects can reselect and refine the nine key indexes among the eighteen indexes.After using Adaboost algorithm to test again,we can find that the nine indexes reduced do not cut down the classification information of the models and the rating corrections do not slide down either.