The　selection　of　the　case　feature　attribute　weights　directly　affects　the　case　retrieval　precision.　According　to　the　existing　problem　of　optimizing　attribute　weights,　a　new　method　based　on　group　decision-making　thought　for　optimizing　the　case　feature　attribute　weights　is　proposed　in　this　paper.　Multiple　sets　of　initial　feature　attribute　weights　are　first　obtained　by　genetic　algorithm.　The　multiple　sets　of　weights　by　group　cardinal　utility　method　are　then　optimized,　and　the　weights　can　be　adaptively　adjusted　during　the　reasoning　process　to　ascertain　reasonable　feature　attribute　weights.　Simulation　results　show　that　the　proposed　approach　can　fully　excavates　the　potential　knowledge　that　exists　in　multiple　sets　of　attribute　weights　and　improves　the　retrieval　precision　of　the　case-based　reasoning　system.