Case　retrieval　is　the　major　step　in　case-based　reasoning　(CBR).　The　similarity　measurement　between　historical　cases　and　the　target　case　is　very　important　in　the　case　retrieval,　and　affects　the　results　of　the　decision.　In　CBR　practical　application,　there　are　hybrid　attribute　values　for　case　attributes.　The　representation　of　the　case　and　performing　case　retrieval　with　high　retrieval　accuracy　for　hybrid　multiple　formats　of　attribute　values　are　significant　challenges,　but　an　in-depth　study　is　lacking.　The　objective　of　this　paper　is　to　develop　a　new　case　retrieval　method　to　hybrid　multi-attribute,　which　contains　four　formats　of　attribute　values,　i.e.,　crisp　numbers,　interval　numbers,　multi-granularity　linguistic　variables,　and　intuitionistic　fuzzy　numbers　(IFNs).　First,　crisp　numbers,　interval　numbers,　and　multi-granularity　linguistic　variables　are　transformed　into　IFNs　and　an　attribute　similarity　measurement　based　on　IFNs　is　proposed.　The　attribute　weights　are　determined　by　an　optimal　matching　model.　This　model　belongs　to　a　type　of　multi-objective　problem　and　can　be　solved　using　the　min-max　method.　Furthermore,　the　case　similarities　between　historical　cases　and　the　target　case　are　obtained　by　aggregating　attribute　similarities　using　evidence　reasoning,　and　the　proper　historical　case(s)　can　be　retrieved　according　to　the　obtained　hybrid　case　similarities.　Finally,　a　case　study　of　the　gas　explosion　in　China＇s　Fujian　province　is　conducted　to　demonstrate　the　proposed　approach　and　its　potential　application.