In　HTTP　adaptive　video　streaming　service,　video　clarity　and　fluency　are　the　two　most　important　Influencing　Factors　(IFs)　affecting　user＇s　Quality　of　Experience　(QoE).　In　this　paper,　a　Video　Clarity-Fluency　Network　(VCFNet)　is　proposed　to　establish　a　QoE　evaluation　model,　which　focuses　on　characterizing　the　clarity-fluency　characteristics　of　video.　Firstly,　the　Harmonic-ResNeXt101　network　is　constructed　by　introducing　the　Harmonic　Network　into　ResNeXt101　to　capture　the　clarity　information　of　video　frames.　The　output　of　the　Fully　Connected　(FC)　layer　of　the　Harmonic-ResNeXt101　network　is　fed　into　the　Gated　Recurrent　Unit　(GRU),　which　is　used　to　perform　short-term　temporal　modeling　to　capture　the　fluency　information　of　video　chunk.　Then,　the　final　output　of　GRU　is　extracted　as　the　clarity-fluency　features,　which　are　concatenated　with　the　statistical　features　of　other　IFs　(including　video　quality　level,　re-buffering　duration,　re-buffering　frequency,　etc.)　to　form　the　feature　parameter　vector　of　IFs.　Finally,　a　neural　network　composed　of　One-Dimensional　Convolutional　Neural　Network　(1D　CNN)　layer　and　two　FC　layers　is　designed　to　establish　the　mapping　relationship　model　between　the　feature　parameter　vector　of　IFs　and　Mean　Opinion　Score　(MOS)　to　predict　user＇s　QoE.　Experimental　results　on　SQoE-III　and　SQoE-IV　datasets　demonstrate　that　the　proposed　VCFNet　can　effectively　capture　the　clarity-fluency　information　of　video,　and　the　resulted　QoE　model　can　achieve　the　state-of-the-art　performance　compared　with　the　existing　QoE　evaluation　models.