Abstract: Studying on the Support Vector Machine （SVM） theory based on reproducing kernel Hibert Space and the wavelet analysis, I constructed the wavelet SVM (WSVM) classifier based on wavelet kernel fuctions. SVM applied hyperspectral classification exists a bottleneck and kernel parameters selection. The wavelet kernel in RKHS is a kind of multidimensional wavelet function that can approximate arbitrary nonlinear functions. Implications on semiparametric estimation are proposed in this paper. By experimented the hyperspectral image with the 64 bands Operational Modular Imaging Spectrometer II (OMIS II) data of Changping Area, Beijing City and ROSIS data of the center of university of Pavia, the classifiers performance and accuracy of WSVM were obtained. In my experiments, the WSVM classifier was demonstrated to be most accurate when it used Coiflet Kernel function of wavelet analysis. Compared with some traditional classifiers (Spectral Angle Mapping classification (SAM) and Minimum Distance classification (MDC)) and classic kernel (Radial Basis Function kernel) of SVM, it indicated that wavelet kernel SVM classifier had the most accurate. Use of the WSVM classifier is a novel approach which improves the accuracy of hyperspectral image classification and expands the possibilities for scientific interpretation and application.