In comparison with single kernel learning， Multiple Kernel Learning （MKL） methods obtain better performance in the tasks of classification and regression. However， all the traditional MKL methods are used for tackling two-class or multi-class classification problems. To make MKL methods fit for dealing with the problems of One-Class Classification （OCC）， a Centered Kernel Alignment （CKA） based multiple kernel One-Class Support Vector Machine （OCSVM） was proposed. Firstly，CKA was utilized to calculate the weight of each kernel matrix， and the obtained weights were used as the linear combination coefficients to linearly combine different types of kernel functions to construct the combination kernel function and introduce them into the traditional OCSVM to replace the single kernel function. The proposed method can not only avoid the selection of kernel function， but also improve the generalization and anti-noise performances. In comparison with other five related methods including OCSVM，Localized Multiple Kernel OCSVM （LMKOCSVM） and Kernel-Target Alignment based Multiple Kernel OCSVM （KTA-MKOCSVM） on 20 UCI benchmark datasets， the geometric mean （g-mean） values of the proposed algorithm were higher than those of the comparison methods on 13 datasets. At the time， the traditional single kernel OCSVM obtained better results on 2 datasets，LMKOCSVM and KTA-MKOCSVM achieved better classification effects on 5 datasets. Therefore， the effectiveness of the proposed method was sufficiently verified by experimental comparisons.