Due to its efficient addressing of non-linear relationship among variables which cannot be extracted by traditional canonical correlation analysis (CCA), kernel canonical correlation analysis (KCCA) has attracted increasing attention. This paper presents an integrated multi-kernel canonical correlation analysis (IMKCCA) method that can efficiently deal with the relationship among variables in multiple data sets. In this method, a special kernel function was constructed, which helped to project the original sample data onto a high-dimensional space; in addition, optimization parameters of SVM was selected to maximize the relationships among multiple groups of data set. Tests on Multiple Feature Data Set illustrated that compared with the traditional CCA and KCCA, IMKCCA based on the optimum parameters of SVM has better performance.