The　existing　keyword-based　search　algorithms　based　on　streaming　data　are　hard　to　meet　the　needs　of　users　for　real-time　data　processing.　To　solve　this　problem,　multi-keyword　parallel　search　algorithm　for　streaming　RDF　data　(MPSASR)　proposed　in　this　paper　combines　the　Spark　and　Redis　frameworks　to　construct　query　subgraphs　integrated　with　ontology　based　on　the　query　keywords　in　real　time.　Associated　with　scoring　function,　regarding　the　high-priority　query　subgraph　as　a　guide,　parallel　search　is　performed　in　the　instance　data,　and　finally　the　Top-k　query　results　are　returned.　Of　course,　our　algorithm　uses　a　hash　compression　algorithm　to　compress　RDF　data,　which　reduces　the　space　required.　Moreover,　our　algorithm　makes　full　use　of　historical　data　and　effectively　speeds　up　search　efficiency.　Our　algorithm　is　experimentally　verified　to　have　great　advantages　in　real-time　search,　response　time,　and　search　effects.　©　Springer　Nature　Singapore　Pte　Ltd.　2018.