The Broad Learning System has been proved to be effective and efficient. However, the associated feature nodes in the system are mainly based on linear mappings. Although such kind of features has been successful in various datasets and applications, more general features (especially for the nonlinear features) are necessary for specific applications. Motivated by the powerful capability of the kernel methods, a novel expansion of broad learning system based on multiple kernels is proposed in this paper. Firstly, the nonlinear feature mappings in the form of multiple kernels are merged into the feature nodes of broad learning system. After that, the resulted features are further enhanced through nonlinear activation functions. The experimental results on UCI datasets indicate that the proposed method outperforms the other methods.