In　tensor-based　multi-view　learning　methods,　the　self-representation　based　subspace　clustering　is　widely　researched,　which　is　effective　but　heavy　in　high　computational　complexity.　Furthermore,　most　of　approaches　learn　the　low-rank　tensor　representation　and　the　final　affinity　matrix　separately　and　ignore　the　difference　between　views.　In　this　paper,　we　construct　the　target　tensor　composed　of　multiple　normalized　similarity　matrices　based　on　the　Gaussian　kernel　function,　which　is　constrained　with　the　t-SVD　based　tensor　nuclear　norm　to　recover　the　low-rank　part.　The　final　affinity　matrix　is　simultaneously　learned　via　weighted　multi-view　fusion　while　optimizing　the　low-rank　tensor,　which　suggests　that　each　view　is　distributed　to　an　adaptive　weight.　Moreover,　the　proposed　method　can　be　extended　to　semi-supervised　classification　through　the　collaborative　optimization　of　the　similarity　tensor　and　the　label　indicator　matrix.　Extensive　experiments　conducted　on　four　real-world　datasets　demonstrate　the　superiority　of　the　proposed　method　compared　with　other　state-of-the-art　methods.　©　2021　IEEE　Computer　Society.　All　rights　reserved.