Feature　selection　reduces　the　dimensionality　of　high-dimensional　data　by　removing　redundant　or　irrelevant　features　from　the　original　features,　thus　reducing　the　negative　impact　of　the　＂dimensionality　curse.＂　Subspace　clustering　feature　selection　methods　focus　on　the　structure　and　properties　within　the　dataset,　so　they　perform　well　in　unsupervised　feature　selection　work.　In　this　paper,　we　sort　out　and　classify　the　research　on　subspace　clustering　feature　selection　and　propose　several　future　research　trends　based　on　the　current　status　of　feature　selection　in　subspace　clustering.