Offline handwritten chinese character recognition (HCCR) is one of means for quick text input and it has a great demand in the area of file recognition, form processing, machine translation and office automation. However it still is a difficult task for handwritten chinese character recognition to put into practical use because of its large stroke change, writing anomaly, and no stroke ranking information can get, etc. al. An efficient classifier occupies very important position for increasing offline HCCR ratio. Support vector machines offer a theoretically well-founded approach to automated learning of pattern classifiers for mining labeled data sets. But as we know, the performance of SVMs largely depend on the kernel function, different kernel function will produce different SVMs, and may result in different performance. However, there are no theories concerning how to choose good kernel functions based on practical using problem. In this paper we make use of the basic properties of Mercer kernel to construct a hybrid kernel from the existing common kernel, and to find the unknown parameters of the hybrid kernel in data-dependent way by minimizing the upper bound of the VC dimension of the set of functions. Our experiment results show that the proposed method is efficient compared with other classifier for handwritten Chinese character recognition.