In　the　scene　where　multiple　sound　sources　simultaneously　occur　in　a　reverberant　room,　it　is　difficult　to　separate　each　sound　source　effectivity.　In　this　paper,　a　multiple　sound　sources　separation　method　based　on　non-negative　matrix　factorization　(NMF)　and　angle　distribution　is　proposed　to　solve　this　problem.　The　proposed　method　can　be　divided　into　two　parts.　The　first　part　uses　the　NMF　to　remove　the　reverberation　components　in　the　signals　with　multiple　sources　sound　simultaneously.　The　second　part　uses　a　method,　which　regards　the　sparse　components　as　the　primary　processing　parts　while　the　non-sparse　zone　is　used　as　the　auxiliary　parts　to　perform　multiple　source　separation.　Specifically,　after　the　dereverberation　step,　the　recorded　signals　are　divided　into　sparse　and　non-sparse　zones　in　the　time-frequency　domain.　The　separation　step　is　processed　according　to　the　different　characteristics　of　these　two　kinds　of　zones.　As　for　the　sparse　zones,　which　consist　of　the　components　from　a　single　source,　the　angular　distribution　probability　statistics　are　used　to　carry　out　the　separation.　For　the　non-sparse　zones,　the　regression　prediction　is　introduced　to　separate　components　from　different　sources　in　the　recorded　signals.　Finally,　the　signals　belonging　to　different　sources　in　the　two　zones　are　properly　matched　and　post-processed.　In　the　multiple　source　separation　method　proposed　in　this　paper,　the　microphone　recording　signal　is　processed　in　layers,　which　reduces　the　computational　complexity　while　improving　the　quality　of　the　separated　sound　source　signals.　Experiments　on　both　simulated　data　and　data　recorded　in　an　actual　acoustic　chamber　have　proved　that　the　proposed　method　could　achieve　good　results,　especially　in　the　actual　scene　with　large　reverberation,　its　performance　is　stable.