This paper presents a novel method for structural data recognition using a large number of graph models. Broadly, existing methods for structral data recognition have two crucial problems: 1) only a single model is used to capture structural variation, 2) naive classification rules are used, such as nearest neighbor method. In this paper, we propose to strengthen both capturing structural variation and the classification ability. The proposed method constructs a large number of graph models and trains decision tree classifiers with the models. There are two contributions of this paper. The first contribution is a novel graph model which can be constructed by straightforward calculation. This calculation enables us to construct many models in feasible time. The second contribution is a novel approach to capture structural variation. We construct a large number of our models in a boosting framework so that we can capture structural variation comprehensively. Consequently, we are able to perform structural data recognition with the powerful classification ability and comprehensive structural variation. In experiments, we show that the proposed method achieves significant results and outperforms the existing methods.