This talk will present a currently fast growing research area of the kernel-based approximation method that covers the interdisciplinary fields of computational mathematics and statistics. The kernel-based method is a vigorous approach of high-dimensional approximation and statistical learning. We will mainly introduce how to apply the kernel-based method for two recent popular research fields about approximating the high-dimensional stochastic partial differential equations, and solving the support vector machines in Banach spaces.