Existing　incomplete　multi-view　learning　models　focus　on　reconstructing　the　latent　variables　of　multiple　views　by　exploring　complementary　and　consistent　information　among　diverse　views.　However,　filling　the　missing　information　for　views　results　in　a　loss　of　consistency,　while　fusion　and　reconstruction　between　views　face　over-fitting　problems.　The　optimal　transport　algorithm　delicately　measures　the　distance　of　two　distributions,　resulting　in　decreased　reconstruction　errors　and　guaranteeing　consistency　and　complementarity　across　multiple　views　of　the　data.　In　light　of　this,　this　paper　proposes　the　incorporation　of　the　optimal　transport　algorithm　into　the　framework　of　incomplete　multi-view　clustering.　The　proposed　consistent　graph　embedding　network　(CGEN-OT)　via　optimal　transport　combines　the　adversarial　module　and　fusion　module　to　learn　a　completed　latent　graph　embedding.　Specifically,　CGEN-OT　utilizes　an　adversarial　module　to　generate　complete　views　and　fuses　them　into　a　consistent　embedding,　and　introduces　reconstruction　loss　and　Sinkhorn　loss　to　jointly　optimize　the　proposed　network　and　obtain　superior　latent　graph　embedding　and　clustering　performance.　To　further　validate　the　clustering　accuracy　and　convergence　of　the　CGEN-OT,　experimental　evaluation　was　conducted　on　six　distinct　incomplete　datasets.　A　comparison　with　existing　state-of-the-art　models　highlights　the　superiority　of　the　proposed　framework.