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Conventional decision tree algorithm employs information gain and information gain ratio to select splitting attribute, and avoid attribute value significance. According to the analysis on a single attribute decision-making problem, it is found that, different value of the same condition attribute has different influence on decision- making results. Based on this preliminary conclusion, proportion matrix and Euclidean norm are introduced to quantitatively describe the important degree of attribute value and a decision tree algorithms proposed based on granular computing . Experimental results show that, compared with ID3 algorithm, the proposed algorithm has higher accuracy when applied to classification problems with multiple attribute values.