The minimum distance classification algorithm and the nearest neighbor classification algorithm are the simplest, most rapid and most effective classification methods, and they are more sensitive to the noise. But to the training samples in few or the training samples that are far from the cluster center, the classification results is poor. To solve this problem, this paper proposes a classification model based on the mean update (MU), by expanding the training sample and updating the mean center to improve the classification results of the test data; and on this basis, it proposes the MU-based minimum distance (MU-MD) classification model, and uses the MU's classification results to recalculate the mean of all test samples, then all test samples are re-divided by using the minimum distance method, so as to determine the final category attribution. This can partially correct misclassification in the MU category process and further improve the classification results.