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To the best of our knowledge, data are often correlated with other data in a dataset, and as a whole, a specific pattern structure is presented from all of the data. However, traditional classification methods (e.g., the support vector machine, SVM) do not take into account the correlation information between pair of data, and classification models are built just by taking advantage of the physical features (e.g., distance or similarity) of the input training data samples. Furthermore, data classification is realized by determining the similarities between the testing data samples and the built classification models in prediction phase. In order to solve the problem on the classification using the individual data information by traditional classification techniques, a hybrid data classification method based on mining the information of data pattern structure (HDCM) is proposed. The proposed classification method consists of two different types of classification techniques, on the one hand, the traditional classification methods based on using sole physical features of data are regarded as common classification methods, and on the other hand, the classification approach based on utilizing the information of data pattern structure is considered as advanced classification methods. In particular the proposed classification method not only has facility in effectively identifying the information of data pattern structure to enhance classification performance, but generalization ability of traditional classification approaches is promoted. A large number of experimental results on synthetic and UCI real-world datasets demonstrate the effectiveness of the proposed classification technique, and better classification performance can be obtained by the proposed classification technique in comparison to traditional classification methods.