Existing　RDF　keyword　search　methods　mainly　search　on　the　large-scale　RDF　data　graph　directly　and　do　not　make　full　use　of　the　semantic　information　in　the　RDF　ontology.　Too　many　iterations　lead　to　unfavorable　search　efficiency　and　unsatisfactory　results.　To　solve　these　problems,　a　distributed　keyword　approximate　search　algorithm　(DKASR)　for　RDF　based　on　Redis　memory　database　cluster　was　proposed　and　the　parallel　search　of　large-scale　data　on　the　distributed　platform　was　realized.　The　algorithm　constructs　ontology　sub-graphs　by　using　the　semantic　information　of　RDF　ontology,　uses　the　semantic　scoring　function　to　sort　ontology　sub-graphs　and　searches　and　returns　the　Top-/k　results　concurrently　with　the　aid　of　MapReduce　computation　model.　If　the　results　do　not　meet　Top-k,　ontology　sub-graphs　are　extended　to　generate　approximate　ontology　sub-graphs　and　the　semantic　similarity　function　is　used　to　sort　approximate　ontology　sub-graphs.　Then.　MapReduce　computation　model　was　used　to　realize　the　parallel　search　until　the　results　meet　Top-/k.　Finally,　the　results　of　experiments　show　that　the　DKASR　algorithm　can　realize　the　RDF　keyword　approximate　search　and　return　the　Top-k　results　efficiently　and　accurately.　©　2017,　Editorial　Department　of　Journal　of　University　of　Science　and　Technology　of　China.　All　rights　reserved.