To　employ　emotion　semantic　of　associated　audio　modal　data　to　guide　extraction　of　highlights　of　video,　a　method,　driven　by　audio　emotion　perception,　is　presented.　An　audio　classifier,　based　on　a　binary-tree　support　vector　machine,　is　employed　to　obtain　the　mid-level　audio　type.　With　an　emotion-mapping　model　integrated,　high-level　emotion　semantic　for　associated　audio　modal　data　is　obtained　finally.　The　complete　audio　emotion　perception　model,　including　an　audio　classifier　and　an　emotion-mapping　model,　is　a　pro-posed　to　analyze　the　emotion　semantic　fluctuation　of　associated　audio.　Furthermore,　video　highlights　are　extracted　with　additional　aids　including　a　start-stop　positioning　strategy　for　highlight　and　a　method　for　audio　video　synchronization.　Taking　emotion　semantic　fluctuation　series　of　audio　as　core　data,　an　entire　video　highlight　extraction　framework,　driven　by　audio-emotion,　is　constructed　with　a　two-stage　emotion　perception　model　of　audio,　which　completes　the　most　important　leading　analysis.　The　experiment　demonstrates　that　the　proposed　framework　can　achieve　high　recall　ratio　and　integrity　with　good　generalized　ability　in　the　case　of　a　certain　guaranteed　accuracy.　©,　2015,　Institute　of　Computing　Technology.　All　right　reserved.