Rapid identification of minerals is the key point for enhancing the efficiency of mineral exploration by remote sensing, mineral mapping by remote sensing and many geological investigations. Because of the limitation of technology and other aspects, the amount of models and software concerning rapid identification of minerals is very small. Since 1990s the development in spectrometers and computers has made it possible to apply near infrared spectrum technology to identify minerals. Two models have emerged. Model Ⅰ is based on analyzing the position of absorption bands, while Model Ⅱ is founded on waveform matching. In the present paper, characteristic spectrum linear inversion modeling was built. Validated by the data gained from end-members of USGS mineral spectrum library by mixing randomly, this model with the accuracy being approximately 100% is much better than Model Ⅰ and Ⅱ. Used to analyze the 23 samples selected in Baogutu area in Xinjiang, the model we built with the accuracy of 64.6% is superior to Model Ⅰ (the accuracy is 33.8%) and Model Ⅱ (the accuracy is 8.1%). Though the accuracy of our model is not as high as that of identification by microscope at present, using our model is much more effective and convenient, and there also will be less artificial error and smaller workload. The good performance of our model in the mineral exploration work by remote sensing in Baogutu area in Xinjiang shows wide popularizing prospects.