The multiclass classification SVM based on the combination of binary tree structures has a small number of binary SVMs, and can avoid the occurrence of inseparable and repellent regions. Since the combination methods of multiclass classification SVM based on binary tree structure lack specific evaluation criteria for category combination, we propose a multiclass classification SVM structure evaluation method based on information gain (IG) ratio of classification attributes, define the IG ratio of classification attributes, and divide multiple classes into left category and right category. We calculate the IG ratio dependent on the classification attribute of variables for each possible combination of categories, and take the maximum value of the IG ratio as the evaluation criterion of this combination. Empirical analysis on Iris in the UCI database shows that the proposed method has a high recognition rate for multiclass classification SVM when the maximum value is taken as the evaluation criterion .