Supernova(SN) is one of the most intense astronomical phenomena among the known stellar activities, but compared with several billion astronomical objects which peoplehave probed, the number of supernova the authors have observed is very small. Therefore, the authors need to find faster and higher-efficiency approaches to searching supernova. In the present paper, we present a novel automated method, which can be successfully used to reduce the range of searching for la supernova candidates in a huge number of galaxy spectra. The theoretical basis of the method is clustering and outlier picking, by in troducing and measuring local outlier factors of datasa mples, description of statistic characters of SN emerges in lowdimen sionspace. Firstly, eigenvectors of Peter’s lasupernova templates are acquired through PCA projection,and the description of la supernova’s statistic characters is calculated. Secondly, in all data set, the local outlier factor(LOF) of each galaxy is calculated including those SN andtheir host galaxy spectra, and all LOFs are arranged in descending order. Finally, spectra with the largest first one percent of allL OFs should be the reduced la SN candidates. Experiments show that this method is arobust and correct range reducing method, which can get rid of the galaxy spectra without supernova component automatically in a flood of galaxy spectra. It is a highly efficient approach to getting the reliable candidates in a spectroscopy survey for followup photometric observation.