Multiple　sound　source　separation　in　a　reverberant　environment　has　become　popular　in　recent　years.　To　improve　the　quality　of　the　separated　signal　in　a　reverberant　environment,　a　separation　method　based　on　a　DOA　cue　and　a　deep　neural　network　(DNN)　is　proposed　in　this　paper.　Firstly,　a　pre-processing　model　based　on　non-negative　matrix　factorization　(NMF)　is　utilized　for　recorded　signal　dereverberation,　which　makes　source　separation　more　efficient.　Then,　we　propose　a　multi-source　separation　algorithm　combining　sparse　and　non-sparse　component　points　recovery　to　obtain　each　sound　source　signal　from　the　dereverberated　signal.　For　sparse　component　points,　the　dominant　sound　source　for　each　sparse　component　point　is　determined　by　a　DOA　cue.　For　non-sparse　component　points,　a　DNN　is　used　to　recover　each　sound　source　signal.　Finally,　the　signals　separated　from　the　sparse　and　non-sparse　component　points　are　well　matched　by　temporal　correlation　to　obtain　each　sound　source　signal.　Both　objective　and　subjective　evaluation　results　indicate　that　compared　with　the　existing　method,　the　proposed　separation　approach　shows　a　better　performance　in　the　case　of　a　high-reverberation　environment.