Multi-view　Representation　Learning　(MRL)　has　recently　attracted　widespread　attention　because　it　can　integrate　information　from　diverse　data　sources　to　achieve　better　performance.　However,　existing　MRL　methods　still　have　two　issues:　(1)　They　typically　perform　various　consistency　objectives　within　the　feature　space,　which　might　discard　complementary　information　contained　in　each　view.　(2)　Some　methods　only　focus　on　handling　inter-view　relationships　while　ignoring　inter-sample　relationships　that　are　also　valuable　for　downstream　tasks.　To　address　these　issues,　we　propose　a　novel　Multi-view　representation　learning　method　with　Dual-label　Collaborative　Guidance　(MDCG).　Specifically,　we　fully　excavate　and　utilize　valuable　semantic　and　graph　information　hidden　in　multi-view　data　to　collaboratively　guide　the　learning　process　of　MRL.　By　learning　consistent　semantic　labels　from　distinct　views,　our　method　enhances　intrinsic　connections　across　views　while　preserving　view-specific　information,　which　contributes　to　learning　the　consistent　and　complementary　unified　representation.　Moreover,　we　integrate　similarity　matrices　of　multiple　views　to　construct　graph　labels　that　indicate　inter-sample　relationships.　With　the　idea　of　self-supervised　contrastive　learning,　graph　structure　information　implied　in　graph　labels　is　effectively　captured　by　the　unified　representation,　thus　enhancing　its　discriminability.　Extensive　experiments　on　diverse　real-world　datasets　demonstrate　the　effectiveness　and　superiority　of　MDCG　compared　with　nine　state-of-the-art　methods.　Our　code　will　be　available　at　https:　//github.com/Bin1Chen/MDCG.