Multi-view　subspace　clustering　aims　to　utilize　the　comprehensive　information　of　multi-source　features　to　aggregate　data　into　multiple　subspaces.　Recently,　low-rank　tensor　learning　has　been　applied　to　multi-view　subspace　clustering,　which　explores　high-order　correlations　of　multi-view　data　and　has　achieved　remarkable　results.　However,　these　existing　methods　have　certain　limitations:　1)　The　learning　processes　of　low-rank　tensor　and　label　indicator　matrix　are　independent.　2)　Variable　contributions　of　different　views　to　the　consistent　clustering　results　are　not　discriminated.　To　handle　these　issues,　we　propose　a　unified　framework　that　integrates　low-rank　tensor　learning　and　spectral　embedding　(ULTLSE)　for　multi-view　subspace　clustering.　Specifically,　the　proposed　model　adopts　the　tensor　singular　value　decomposition　(t-SVD)　based　tensor　nuclear　norm　to　encode　the　low-rank　property　of　the　self-representation　tensor,　and　a　label　indicator　matrix　via　spectral　embedding　is　simultaneously　exploited.　To　distinguish　the　importance　of　various　views,　we　learn　a　quantifiable　weighting　coefficient　for　each　view.　An　effective　recursion　optimization　algorithm　is　also　developed　to　address　the　proposed　model.　Finally,　we　conduct　comprehensive　experiments　on　eight　real-world　datasets　with　three　categories.　The　experimental　results　indicate　that　the　proposed　ULTLSE　is　advanced　over　existing　state-of-the-art　clustering　methods.