In　this　paper,　we　propose　a　deep　multimodal　feature　learning　(DMFL)　network　for　RGB-D　salient　object　detection.　The　color　and　depth　features　are　firstly　extracted　from　low　level　to　high　level　feature　using　CNN.　Then　the　features　at　the　high　layer　are　shared　and　concatenated　to　construct　joint　feature　representation　of　multi-modalities.　The　fused　features　are　embedded　to　a　high　dimension　metric　space　to　express　the　salient　and　non-salient　parts.　And　also　a　new　objective　function,　consisting　of　cross-entropy　and　metric　loss,　is　proposed　to　optimize　the　model.　Both　pixel　and　attribute　level　discriminative　features　are　learned　for　semantical　grouping　to　detect　the　salient　objects.　Experimental　results　show　that　the　proposed　model　achieves　promising　performance　and　has　about　1%　to　2%　improvement　to　conventional　methods.