The monotonic decision tree algorithm of interval-valued attributes is one of the important ways to deal with the classification problems with monotonicity constraints. However, the correlation between attributes is not taken into account in the process of building a decision tree, so it is very possible that over-classification of redundant attributes has little or no significance. To solve these problems, based on the monotonic decision tree algorithm of interval-valued attributes, the paper analyzes the influence of redundant information between interval-valued attributes on the construction of monotonic decision tree, and proposes an extended monotonic decision tree algorithm of interval-valued attributes. The extended attributes are selected by maximizing the value of the rank mutual information between the candidate attributes and the decision attribute and minimizing the value of the rank mutual information between the candidate attributes and the selected attributes on the same branch. The experimental results show that the extended algorithm can avoid repeated selection of the same attributes after considering the correlation among the condition attributes. Compared with the existing algorithms, the extended algorithm can construct a better monotonic decision tree.