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Raman spectra and ultraviolet-visible absorption spectra of four different geographic origins of Radix Astragali were collected. These data were analyzed using kernel principal component analysis combined with sparse representation classification. The results showed that the recognition rate reached 70.44% using Raman spectra for data input and 90.34% using ultraviolet-visible absorption spectra for data input. A new fusion method based on Raman combined with ultraviolet-visible data was investigated and the recognition rate was increased to 96.43%. The experimental results suggested that the proposed data fusion method effectively improved the utilization rate of the original data. [ABSTRACT FROM AUTHOR]