In order to study the adaptability of using different kinds of spectral information for quick analysis of the quality of radix scutellariaes(RS)，the contents of baicalin in 73 batches of RS determined by high performance liquid chromatography(HPLC) were used as dependent variables(y)，the near infrared spectra(NIRs)，ultraviolet-visible spectra(UV-Vis) and multi-source complex spectral(MSCs) information including UV-visible and near infrared spectra were used as independent variables(x).The partial least square regression(PLSR) and a novel method called as keeping same relationship between X and Y space on K nearest neighbors(KNN-KSR for short) were applied to predict the contents of baicalin in the RS samples based on above three kinds of spectral information.By comparing the root mean square error of prediction(RMSEP)，the average relative error(MRE) and correlation coefficient(R) between the measured and predicted values in validation set were applied to evaluate prediction precision. Regardless of the types of spectral information，the prediction precision of KNN-KSR method was always better than the PLSR method. The analysis results of baicalin based on NIRs were the best，and those based on the UV-visible spectra were the second. Although the prediction error of baicalin contents was the biggest based on the multi-source complex spectral information，it was still lower than 6%.The error satisfied the requirement of industrial analysis. The multi-source complex spectrometer has the advantages of small volume，light weight，low cost and portability. It is expected to improve the analytical precision of the instrument by optimizing its wavelengths and modeling method，so that it could be adapt to rapid spot acquisition of more herbs，and to analyze and monitor the quality of follow-up products.