Clustering algorithm is an important algorithm used to find the data distribution and implicit scheme in data mining. It can study spectra of large amount, multi-parameter and categories unknown simply and effectively. Using lick index as the eigenvalues of spectra can effectively improve the speed to calculate the high-dimensional spectra which can also retain more astrophysical characteristics of spectra. This paper finishes clustering of the survey data with k-means algorithm, using lick index as the eigenvalues of data with finished analysis results. The results show that the new method can gather data with similar physical characteristics together quicker and efficiently, with very good results in discovering rare stars. This method can be applied to the study of Survey data.