To　solve　the　problem　of　unreasonable　distribution　of　feature　weights　in　the　data-based　prediction　model,　the　whale　optimization　algorithm　was　included　in　the　population　space　of　cultural　algorithm,　and　a　cultural　whale　optimization　algorithm　(CWOA)　was　acquired　for　optimal　distribution　of　feature　weights.　First,　the　root　mean　square　error　of　the　prediction　model　was　used　as　the　fitness　function,　and　the　whale　optimization　algorithm　was　utilized　to　iteratively　refine　the　feature　weight　in　the　population　space.　Then,　the　optimal　weight　in　the　population　space　was　placed　in　the　belief　space　through　the　defined　acceptance　function　for　performance　evaluation　and　double　mutation　evolution　so　as　to　form　situational　knowledge　and　normative　knowledge.　Finally,　the　influence　function　was　used　to　update　and　guide　the　weights　in　the　population　space　to　further　obtain　the　optimal　allocation　results　of　feature　weights.　By　taking　the　prediction　model　on　the　basis　of　case-based　reasoning　as　an　example,　University　of　California　Irvine　(UCI)　standard　data　set　was　used　to　conduct　the　contract　experiments　on　different　distribution　techniques　for　feature　weights.　Results　show　that　the　prediction　accuracy　of　the　technique　mentioned　in　this　paper　after　weight　distribution　is　optimal,　delivering　certain　application　value　in　the　field　of　machine　learning　involving　the　distribution　of　feature　weights.　©　2021,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.