Without　the　valuable　label　information　to　guide　the　learning　process,　it　is　demanding　to　fully　excavate　and　integrate　the　underlying　information　from　different　views　to　learn　the　unified　multi-view　representation.　This　paper　focuses　on　this　challenge　and　presents　a　novel　method,　termed　Graph-guided　Unsupervised　Multi-view　Representation　Learning　(GUMRL),　taking　full　advantage　of　multi-view　graph　information　during　the　learning　process.　To　be　specific,　GUMRL　jointly　conducts　the　view-specific　feature　representation　learning,　which　is　under　the　guidance　of　graph　information,　and　the　unified　feature　representation　learning,　which　fuses　the　underlying　graph　information　of　different　views　to　learn　the　desired　unified　multi-view　feature　representation.　Regarding　downstream　tasks,　such　as　clustering　and　classification,　the　classic　single-view　algorithms　can　be　directly　performed　on　the　learned　unified　multi-view　representation.　The　designed　objective　function　is　effectively　optimized　based　on　an　alternating　direction　minimization　method,　and　experiments　conducted　on　six　real-world　multi-view　datasets　show　the　effectiveness　and　competitiveness　of　our　GUMRL,　compared　to　several　state-of-the-art　methods.