Hyperspectral　unmixing　is　one　of　the　most　important　techniques　in　hyperspectral　remote　sensing　image　analysis.　During　the　past　decades,　many　models　have　been　widely　used　in　hyperspectral　unmixing,　such　as　nonnegative　matrix　factorization　(NMF)　model,　sparse　regression　model,　etc.　Most　recently,　a　new　matrix　factorization　model,　deep　matrix,　is　proposed　and　shows　good　performance　in　face　recognition　area.　In　this　paper,　we　introduce　the　deep　matrix　factorization　(DMF)　for　hyperspectral　unmixing.　In　this　method,　the　DMF　method　is　applied　for　hyperspectral　unmixing.　Compared　with　the　traditional　NMF-based　unmixing　methods,　DMF　could　extract　more　information　with　multiple-layer　structures.　An　optimization　algorithm　is　also　proposed　for　DMF　with　two　designed　processes.　Results　on　both　synthetic　and　real　data　have　validated　the　effectiveness　of　this　method,　and　shown　that　it　has　outperformed　several　state-of-the-art　unmixing　approaches.