The　combination　of　neural　networks　and　beamforming　has　proven　very　effective　in　multi-channel　speech　separation,　but　its　performance　faces　a　challenge　in　complex　environments.　In　this　paper,　an　iteratively　refined　multi-channel　speech　separation　method　is　proposed　to　meet　this　challenge.　The　proposed　method　is　composed　of　initial　separation　and　iterative　separation.　In　the　initial　separation,　a　time-frequency　domain　dual-path　recurrent　neural　network　(TFDPRNN),　minimum　variance　distortionless　response　(MVDR)　beamformer,　and　post-separation　are　cascaded　to　obtain　the　first　additional　input　in　the　iterative　separation　process.　In　iterative　separation,　the　MVDR　beamformer　and　post-separation　are　iteratively　used,　where　the　output　of　the　MVDR　beamformer　is　used　as　an　additional　input　to　the　post-separation　network　and　the　final　output　comes　from　the　post-separation　module.　This　iteration　of　the　beamformer　and　post-separation　is　fully　employed　for　promoting　their　optimization,　which　ultimately　improves　the　overall　performance.　Experiments　on　the　spatialized　version　of　the　WSJ0-2mix　corpus　showed　that　our　proposed　method　achieved　a　signal-to-distortion　ratio　(SDR)　improvement　of　24.17　dB,　which　was　significantly　better　than　the　current　popular　methods.　In　addition,　the　method　also　achieved　an　SDR　of　20.2　dB　on　joint　separation　and　dereverberation　tasks.　These　results　indicate　our　method＇s　effectiveness　and　significance　in　the　multi-channel　speech　separation　field.