In this paper, a new domain adaptation learning method is proposed by jointing weighted balanced feature matching and instance reweighting based on kernel extreme learning machine (KELM). Specifically, for the weighted balance feature matching, it can not only adapt the importance of the difference between the marginal and the condition, but also deal with the class imbalances adaptively for the domain adaptation learning. In addition, the instance reweighting is combined to reduce the effect of unrelated instances on inter domain differences in source domain instances. The experimental results show the performance of the proposed approach is better than the state-of-the-art approaches in terms of the classification accuracy and operation speed, especially in the speed of operation is particularly prominent.