DASH　(Dynamic　Adaptive　Streaming　over　HTTP　(HyperText　Transfer　Protocol))　as　a　universal　unified　multimedia　streaming　standard　selects　the　appropriate　video　bitrate　to　improve　the　user＇s　Quality　of　Experience　(QoE)　according　to　network　conditions,　client　status,　etc.　Considering　that　the　quantitative　expression　of　the　user＇s　QoE　is　also　a　difficult　point　in　itself,　this　paper　researched　the　distortion　caused　due　to　video　compression,　network　transmission　and　other　aspects,　and　then　proposes　a　video　QoE　metric　for　dynamic　adaptive　streaming　services.　Three-Dimensional　Convolutional　Neural　Networks　(3D　CNN)　and　Long　Short-Term　Memory　(LSTM)　are　used　together　to　extract　the　deep　spatial-temporal　features　to　represent　the　content　characteristics　of　the　video.　While　accounting　for　the　fluctuation　in　the　quality　of　a　video　caused　by　bitrate　switching　on　the　QoE,　other　factors　such　as　video　content　characteristics,　video　quality　and　video　fluency,　are　combined　to　form　the　input　feature　vector.　The　ridge　regression　method　is　adopted　to　establish　a　QoE　metric　that　enables　to　dynamically　describe　the　relationship　between　the　input　feature　vector　and　the　value　of　the　Mean　Opinion　Score　(MOS).　The　experimental　results　on　different　datasets　demonstrate　that　the　prediction　accuracy　of　the　proposed　method　can　achieve　superior　performance　over　the　state-of-the-art　methods,　which　proves　the　proposed　QoE　model　can　effectively　guide　the　client＇s　bitrate　selection　in　dynamic　adaptive　streaming　media　services.