In many decision making tasks,the features and decision are ordinal.Several ordinal classification learning algorithms have been developed in recent years,it is shown that these algorithms are sensitive to noisy samples and do not work in real-world applications.In this work,we propose a new measure of feature quality, called rank mutual information.Then,we design an ordinal decision tree(REOT) construction technique based on rank mutual information.The theoretic and experimental analysis shows that the proposed algorithm is effective. In many decision making tasks, the features and decision are ordinal. Semantic ordinal classification learning algorithms have been developed in recent years, it is shown that these algorithms are sensitive to noisy samples and do not work in real-world applications. In this work, we propose a new measure of feature quality, called rank mutual information. Chen, we design an ordinal decision tree (REOT) construction technique based on rank mutual information. The theoretic and experimental analysis shows that the proposed algorithm is effective.