For　mobile　streaming　media　service　providers,　it　is　necessary　to　accurately　predict　the　quality　of　experience　(QoE)　to　formulate　appropriate　resource　allocation　and　service　quality　optimization　strategies.　In　this　paper,　a　QoE　evaluation　model　is　proposed　by　considering　various　influencing　factors　(IFs),　including　perceptual　video　quality,　video　content　characteristics,　stalling,　quality　switching　and　video　genre　attribute.　Firstly,　a　no-reference　video　multimethod　assessment　fusion　(VMAF)　model　is　constructed　to　measure　the　perceptual　quality　of　the　video　by　the　deep　bilinear　convolutional　neural　network.　Then,　the　deep　spatio-temporal　features　of　video　are　extracted　using　a　TSM-ResNet50　network,　which　incorporates　temporal　shift　module　(TSM)　with　ResNet50,　obtaining　feature　representation　of　video　content　characteristics　while　balancing　computational　efficiency　and　expressive　ability.　Secondly,　video　genre　attribute,　which　reflects　the　user’s　preference　for　different　types　of　videos,　is　considered　as　a　IF　while　constructing　the　QoE　model.　The　statistical　parameters　of　other　IFs,　including　the　video　genre　attribute,　stalling　and　quality　switching,　are　combined　with　VMAF　and　deep　spatio-temporal　features　of　video　to　form　an　overall　description　parameters　vector　of　IFs　for　formulating　the　QoE　evaluation　model.　Finally,　the　mapping　relationship　model　between　the　parameters　vector　of　IFs　and　the　mean　opinion　score　is　established　through　designing　a　deep　neural　network.　The　proposed　QoE　evaluation　model　is　validated　on　two　public　video　datasets:　WaterlooSQoE-III　and　LIVE-NFLX-II.　The　experimental　results　show　that　the　proposed　model　can　achieve　the　state-of-the-art　QoE　prediction　performance.　©　2022,　The　Author(s),　under　exclusive　licence　to　Springer　Science+Business　Media,　LLC,　part　of　Springer　Nature.