Cross-Domain　Recommendation　has　been　popularly　studied　to　resolve　data　sparsity　problem　via　leveraging　knowledge　transfer　across　different　domains.　In　this　paper,　we　focus　on　the　Unified　Cross-Domain　Recommendation　(Unified　CDR)　problem.　That　is,　how　to　enhance　the　recommendation　performance　within　and　cross　domains　when　users　are　partially　overlapped.　It　has　two　main　challenges,　i.e.,　1)　how　to　obtain　robust　matching　solution　among　the　whole　users　and　2)　how　to　exploit　consistent　and　accurate　results　across　domains.　To　address　these　two　challenges,　we　propose　MUCRP,　a　cross-domain　recommendation　framework　for　the　Unified　CDR　problem.　MUCRP　contains　three　modules,　i.e.,　variational　rating　reconstruction　module,　robust　variational　embedding　alignment　module,　and　cycle-consistent　preference　extraction　module.　To　solve　the　first　challenge,　we　propose　fused　Gromov-Wasserstein　distribution　co-clustering　optimal　transport　to　obtain　more　robust　matching　solution　via　considering　both　semantic　and　structure　information.　To　tackle　the　second　challenge,　we　propose　embedding-consistent　and　prediction-consistent　losses　via　dual　autoencoder　framework　to　achieve　consistent　results.　Our　empirical　study　on　Douban　and　Amazon　datasets　demonstrates　that　MUCRP　significantly　outperforms　the　state-of-the-art　models.