Multi-data　analysis　and　reasoning　techniques　were　adopted　to　improve　the　forecasting　speed　and　reliability　of　emergency　resources　demand.　Firstly,　based　on　the　historical　case　information,　the　rescue　case　index　weights　were　calculated.　Then　an　algorithm　combining　fuzzy　C-means　clustering　with　case　retrieval　was　established　to　increase　the　efficiency　of　case　retrieval,　which　was　performed　by　CBR　(casebased　reason)　similarity　and　GRA　(grey　relational　analysis)　correlation.　After　the　CBR　similarity　vector　and　GRA　correlation　vector　were　obtained,　the　grey　relational　analysis　was　used　to　calculate　the　similarity-correlation　vector　so　as　to　ensure　that　similar　cases　are　retrieved　efficiently.　Finally,　a　resources　demand　model　was　built　up.　The　results　confirmed　that　case　clustering　to　achieve　preliminary　data　filtering　can　enhance　retrieval　speed　and　combining　two　retrieval　methods　can　improve　the　reliability　of　retrieval.　©　2016,　Editorial　Department　of　Journal　of　Northeastern　University.　All　right　reserved.