Live　video　streaming　platforms　have　attracted　millions　of　streamers　and　daily　active　users.　For　profit　and　popularity　accumulation,　some　streamers　mix　pornography　content　into　live　content　to　avoid　online　supervision.　Therefore,　accurate　recognition　of　porn　streamers　in　live　video　streaming　has　become　a　challenging　task.　Porn　streamers　in　live　video　present　multimodal　characteristics　including　visual　and　acoustic　content.　Therefore,　a　porn　streamer　recognition　method　in　live　video　streaming　is　proposed　that　uses　attention-gated　multimodal　deep　features.　Our　contribution　includes　the　following:　(1)　multimodal　deep　features,　i.e.,　spatial,　motion　and　audio,　are　extracted　from　live　video　streaming　using　convolutional　neural　networks　(CNNs),　in　which　the　temporal　context　of　multimodal　features　is　obtained　with　a　bi-directional　gated　recurrent　unit　(Bi-GRU);　(2)　the　tri-attention　gated　mechanism　is　applied　to　map　the　associations　between　different　modalities　by　assigning　higher　weights　to　important　features　for　further　reduction　in　the　redundancy　of　multimodal　features;　(3)　porn　streamers　in　live　video　streaming　are　recognized　via　the　attention-gated　multimodal　deep　features.　Six　experiments　are　conducted　on　a　real-world　dataset,　and　the　competitive　results　demonstrate　that　our　method　can　effectively　recognize　porn　streamers　in　live　video　streaming.