With the development of highly efficient graph data collection technology in many scientific application fields, classification of graph data becomes an important topic in the machine learning and data mining community. At present, many graph classification approaches have been proposed. Some of the graph classification approaches take three steps, which are mining frequent subgraphs, selecting feature subgraphs from mined frequent subgraphs, and constructing classification model by frequent subgraphs. Some other graph classification approaches take two steps, which are mining discriminative subgraphs directly from graph data and learning classification model by discriminative subgraphs. However, during mining frequent subgraphs or discriminative subgraphs, these approaches only take advantage of the structural information of the pattern, and do not consider the embedding information. In fact, in some efficient subgraph mining algorithms, the embedding information of a pattern can be maintained. We propose a graph classification approach, in which we employ a novel subgraph encoding approach with category label and adopt a feature subgraph selection strategy based on category information. Meanwhile, during mining frequent subgraphs, we make full use of embedding sets to select the feature subgraphs and by only one step we are able to generate classification rules. Experiment results show that the proposed approach is effective and feasible for classifying chemical compounds.