Feature　weights　have　a　significant　effect　on　the　accuracy　of　case-based　reasoning　(CBR)　prediction　models.　Therefore,　this　paper　proposes　a　feature　weight　optimization　method　combining　a　t-distribution　mutation　operator　and　a　memetic　algorithm　(t-memetic).　In　this　method,　the　mean　absolute　percentage　error　of　the　CBR　prediction　model　is　defined　as　a　fitness　function,　and　the　memetic　framework　is　used　to　realize　the　optimization　process　for　case　weights.　A　sparrow　search　algorithm　and　adaptive　t-distribution　mutation　operator　are　used　to　realize　the　global　search　of　the　case　feature　weights,　and　the　simulated　annealing　algorithm　is　used　to　perform　a　local　search　of　the　weighted　individuals　with　the　best　fitness　for　the　current　population.　Five　UCI　standard　regression　datasets　are　used　respectively　to　test　the　effectiveness　of　the　proposed　method　and　compare　it　with　classical　feature　weight　optimization　algorithms.　The　results　show　that　the　CBR　prediction　model　has　the　highest　accuracy　after　the　weights　are　optimized　by　the　t-memetic　algorithm,　which　indicates　that　the　proposed　weight　optimization　method　can　be　used　effectively　in　CBR　prediction　models.