The　performance　of　existing　speech　enhancement　algorithms　based　on　deep　learning　is　not　ideal　in　complex　noise　environment.　To　improve　the　problem,　a　bidirectional　optimized　hybrid　network　named　BLSTM-DNN　is　constructed　based　on　bidirectional　long-short　term　memory　(BLSTM)　network　and　fully-connected　deep　neural　network　(DNN).　This　structure　uses　BLSTM　to　extract　high-level　information　including　past　and　future　temporal　context　of　noisy　speech.　Next,　fully-connected　DNN　fits　the　high-level　information　to　ideal　ratio　mask　(IRM).　Finally,　the　IRMs　estimated　by　the　BLSTM-DNN　are　used　to　enhance　the　noisy　speech.　Experimental　results　show　that　the　proposed　method　can　effectively　improve　the　speech　quality　and　intelligibility　under　unknown　noise　conditions.