The method of spectral data analysis, which can remove a lot of redundancy of high-dimensional spectral data and extract its characteristic spectrum, is an important foundation for the widespread application of spectral instruments. The contradiction of the applicability of the heterogeneity and spectral characteristics of the method of universal selection, to a certain extent, restricts the application of spectral instruments, need to be resolved. In this paper, a sequential forward selection (SFS) spectral feature adaptive data mining method is proposed to generate the optimal combination of variables as support vector machine (SVM) classification model input, to achieve the spectral data reduction and obtain a highprecision data classification. This method can effectively solve the problem of multi-class classification of a large number of spectral data, which is proved and applied in the classification of mahogany. It provides a new way to solve the difficulty of subjective experience feature selection in height-aliasing of spectral peaks. 