Video　is　a　rich　media　widely　used　in　many　of　our　daily　life　applications　like　education,　entertainment,　surveillance,　etc.　In　order　to　retrieve　rapidly,　it　is　necessary　to　establish　digital　archive　for　storing　these　videos.　However,　it　is　not　realistic　to　store　vast　amounts　of　video　data　into　digital　archive　artificially.　This　paper　proposes　a　new　method　for　the　task　of　video　digital　archive　management　by　employing　scene　recognition　technology　based　on　extreme　learning　machine　(ELM).　This　paper　only　focuses　on　scene　recognition　technology　which　is　the　key　step　of　digital　video　archive　management.　Dense　scale　invariant　feature　transform　(dense　SIFT)　features　are　used　as　features　in　this　proposed　method.　The　15-Scenes　dataset　with　more　than　4000　images　is　used.　Experimental　results　have　shown　that　this　proposed　method　achieves　not　only　high　recognition　accuracy　but　also　extremely　low　computational　cost.　©　2015　IEEE.