In　this　work,　we　devote　ourselves　to　the　challenging　task　of　Unsupervised　Multi-view　Representation　Learning　(UMRL),　which　requires　learning　a　unified　feature　representation　from　multiple　views　in　an　unsupervised　manner.　Existing　UMRL　methods　mainly　focus　on　the　learning　process　within　the　feature　space　while　ignoring　the　valuable　semantic　information　hidden　in　different　views.　To　address　this　issue,　we　propose　a　novel　approach　called　Semantically　Consistent　Multi-view　Representation　Learning　(SCMRL),　which　aims　to　excavate　underlying　multi-view　semantic　consensus　information　and　utilize　it　to　guide　the　unified　feature　representation　learning　process.　Specifically,　SCMRL　consists　of　a　within　view　reconstruction　module　and　a　unified　feature　representation　learning　module.　These　modules　are　elegantly　integrated　using　a　contrastive　learning　strategy,　which　serves　to　align　the　semantic　labels　of　both　view-specific　feature　representations　and　the　learned　unified　feature　representation　simultaneously.　This　integration　allows　SCMRL　to　effectively　leverage　consensus　information　in　the　semantic　space,　thereby　constraining　the　learning　process　of　the　unified　feature　representation.　Compared　with　several　state-of-the-art　algorithms,　extensive　experiments　demonstrate　its　superiority.　Our　code　is　released　on　https://github.com/YiyangZhou/SCMRL.&　COPY;　2023　Elsevier　B.V.　All　rights　reserved.