The classification of imbalanced datasets is one of the important topics in machine learning. Most of the existing imbalance learning algorithms designed for dichotomies are insufficient to meet multi-class classification needs. To tackle multi-class imbalance classification problem， we design a new multi-classification model synthesizing rough sets， resampling methods and dynamic ensemble classification strategy in this study. The model utilizes the hybrid sampling and the rough set reduction algorithm to generate multiple balanced data subsets， on which the construction of the dynamic ensemble classification model is realized. The experiments on 22 real datasets demonstrate that the designed method has higher prediction performance on identifying minority samples compared with two previous algorithms， which can be an alternative selection strategy in multi-class imbalance classification.