Classification of multi-valued and multi-labeled data is about a sample which is not only associated with a set of labels, but also with several values that include some attributes. This paper proposes a multi-valued and multi-labeled learning framework that combines multi-value decomposition with multi-label learning (MDML), using four strategies to deal with multi-valued attributes and three classical, multi-label algorithms to learn. Experimental results demonstrate that MDML significantly outperforms the decision tree based method. Meanwhile, combined methods can be applied to various types of datasets.