The　problem　of　identifying　spectra　collected　by　large　sky　survey　telescope　is　urgent　to　study　to　help　astronomers　discover　new　celestial　bodies.　Due　to　spectral　data　characteristics　of　high-dimension　and　volume,　principle　component　analysis　(PCA)　technique　is　commonly　used　for　extracting　features　and　saving　operations.　Like　many　other　matrix　factorization　methods,　PCA　lacks　intuitive　meaning　because　of　its　negativity.　In　this　paper,　non-negative　matrix　factorization　(NMF)　technique　distinguished　from　PCA　by　its　use　of　nonnegative　constrains　is　applied　to　stellar　spectral　type　classification.　Firstly,　NMF　was　used　to　extract　features　and　compress　data.　Then　an　efficient　classifier　based　on　distance　metric　was　designed　to　identify　stellar　types　using　the　compressed　data.　The　experiment　results　show　that　the　proposed　method　has　good　performance　over　more　than　70,000　real　stellar　data　of　Sloan　Digital　Sky　Survey　(SDSS).　And　the　method　is　promising　for　large　sky　survey　telescope　projects.