Driven　by　the　complementarity　and　consistency　inherent　in　multiview　data,　multiview　clustering　(MVC)　has　garnered　widespread　attention　in　various　domains.　Real-world　data　often　encounters　the　issue　of　missing　information,　leading　to　a　surge　of　interest　in　the　domain　of　incomplete　MVC　(IMVC).　Despite　existing　approaches　having　made　significant　progress　in　addressing　IMVC,　two　significant　challenges　persist:　1)　many　alignment-based　methodologies　tend　to　overlook　the　topological　relationships　among　instances　and　2)　the　view　representations　based　on　completion　lack　reconstructive　properties,　casting　doubt　on　their　alignment　with　the　actual　view　representations.　In　response,　we　present　a　novel　approach　termed　neighbor-based　completion　for　addressing　IMVC　(NBIMVC),　which　capitalizes　on　the　topological　information　among　instances　and　the　consistent　information　across　views.　Specifically,　our　method　uses　autoencoders　to　learn　feature　representations　for　each　view　and　leverages　nearest-neighbor　relationships　between　unique　and　complete　instances　to　complete　missing　features　in　missing　views.　Subsequently,　we　enforce　hard　negative　alignment　constraints　on　complete　paired　instances　in　the　feature　space.　Finally,　we　ensure　the　consistency　of　views　in　the　semantic　space　by　employing　cluster　information　and　a　shared　clustering　network,　which　facilitates　the　final　multiview　categories　output　and　effectively　resolves　the　IMVC　problem.　Extensive　experimental　evaluations　validate　the　efficacy　of　our　proposed　method,　showcasing　comparable　or　superior　performance　to　existing　approaches.