Provenance　classification　of　nephrite　jades　is　important　since　the　unit　price　of　jade　changes　drastically　with　its　geological　origin.　In　the　present　work,　a　detailed　comparison　between　commonly　applied　multivariate　methods　is　conducted　to　classify　nephrite　samples　from　five　different　locations　via　their　laser-induced　breakdown　spectroscopy　(LIBS)　spectra.　Five　multivariate　methods　including　principal　component　analysis　(PCA),　one-step　and　pairwise　partial　least　squares　discriminant　analysis　(PLS-DA),　linear　discriminant　analysis　(LDA),　and　support　vector　machine　(SVM)　were　applied　to　provide　the　classification　information　on　the　samples.　PCA　was　used　for　rough　classification　while　the　classification　performance　of　the　other　four　methods　was　discussed　in　detail.　The　results　show　a　training　accuracy　of　89.0%,　99.2%,　99.8%　and　100%,　and　a　testing　accuracy　of　76.8%,　97.8%,　92.8%　and　99.3%　for　the　PLS-DA　(one-step),　PLS-DA　(pairwise),　LDA　and　SVM　algorithms　respectively.　The　superior　model　nature　and　the　selection　of　suitable　characteristic　lines　for　weight　differences　led　to　the　high　performance　of　SVM,　showing　an　excellent　applicability　for　the　provenance　classification　of　nephrite　jades　using　LIBS　spectra.