Deep　multi　-view　subspace　clustering　has　achieved　increasing　attention　owing　to　its　encouraging　ability　to　address　the　nonlinear　multi　-view　data.　Despite　significant　progress,　most　existing　deep　multi　-view　subspace　clustering　methods　still　suffer　from　the　following　drawbacks.　First,　they　usually　focus　on　utilizing　the　last　-level　feature　from　the　last　encoder　layer　of　the　auto　-encoder　related　to　each　view,　yet　often　overlook　exploiting　the　earlier　-level　features　from　the　earlier　encoder　layers　as　well　as　integrating　the　multi　-level　features　from　multiple　encoder　layers.　Second,　when　learning　the　subspace　representation　from　the　self　-expressive　layer,　they　rarely　consider　the　cleanliness　and　robustness　of　the　learned　subspace　representation.　To　overcome　these　drawbacks,　this　paper　proposes　a　novel　c　lean　and　r　obust　m　ulti　-level　s　ubspace　r　epresentations　l　earning　for　deep　multi　-view　subspace　clustering　(CRMSRL).　Specifically,　the　auto　-encoder　is　adopted　to　nonlinearly　map　each　view　into　the　multi　-level　features,　and　then　multiple　self　-expressive　layers　are　added　between　the　encoder　layers　and　their　corresponding　decoder　layers　to　provide　multiple　information　flow　paths　through　the　auto　-encoder　and　learn　the　multi　-level　subspace　representations　corresponding　to　the　multi　-level　features.　Consequently,　the　intricate　hierarchical　information　embedded　in　the　multi　-level　features　is　passed　to　the　corresponding　multilevel　subspace　representations.　Moreover,　with　the　idea　of　robust　principal　component　analysis　(RPCA),　the　learned　multi　-level　subspace　representations　can　be　further　purified　to　achieve　the　cleaner　and　more　robust　ones.　In　addition,　to　excavate　the　structural　consistency　among　different　views,　the　common　layer　is　designed　to　interact　with　different　view　-specific　self　-expressive　layers　and　achieve　the　high　-quality　common　subspace　representation,　which　can　be　applied　to　the　spectral　clustering　algorithm　to　obtain　the　final　clustering　results.　Experimental　results　show　that　CRMSRL　outperforms　twelve　state-of-the-art　baseline　methods　on　six　benchmark　datasets.　In　particular,　CRMSRL　achieves　4.5%　improvements　on　the　MSRCV1　dataset　in　terms　of　accuracy,　compared　with　the　best　baseline　method.