The present paper used the emissivity of non-processed rocks measured by M304, a hyperspectral Fourier transform infrared (FTIR) spectroradiometer, and SiO2 content by the X-ray fluorescence spectrometry. After continuum removal and normalization, stepwise regress method was employed to select the feature bands of rocks emissivity. And then quantitative relationship between SiO2 content and continuum removal emissivity of feature bands was analysed. Based on that, by comparing twelve SiO2 indices models, the optimal model for predicting SiO2 content was built. The result showed that the SiO2 indices can predict SiO2 content efficiently, and especially the normalization silicon dioxide index (NSDI) about 11.18 and 12.36 microm is the best; compared with regression models, NSDI is simpler and has higher practicality; the result has an important application value in rock classification and SiO2 content extraction with high precision.