Blind　quality　assessment　for　user-generated　content　(UGC)　or　consumer　videos　is　challenging　in　computer　vision.　Two　open　issues　are　yet　to　be　addressed:　how　to　effectively　extract　high-dimensional　spatial-temporal　features　of　consumer　videos　and　how　to　appropriately　model　the　relationship　between　these　features　and　user　perceptions　within　a　unified　blind　video　quality　assessment　(BVQA).　To　tackle　these　issues,　we　propose　a　novel　BVQA　model　with　spatial-temporal　perception　and　fusion.　Firstly,　we　develop　two　perception　modules　to　extract　the　perceptual-distortion-related　features　separately　from　the　spatial　and　temporal　domains.　In　particular,　the　temporal-domain　features　are　obtained　with　a　combination　of　3D　ConvNet　and　residual　frames　for　their　high　efficiencies　in　capturing　the　motion-specific　temporal　features.　Secondly,　we　propose　a　feature　fusion　module　that　adaptively　combines　spatial-temporal　features.　Finally,　we　map　the　fused　features　onto　perceptual　quality.　Experimental　results　demonstrate　that　our　model　outperforms　other　advanced　methods　in　conducting　subjective　video　quality　prediction.