Geographical　knowledge　cloud　service　is　a　typical　online　service　that　provides　big　spatial　data　analysis　with　the　function　of　knowledge　discovery　or　decision-making.　The　composition　of　geographical　knowledge　cloud　service　imposes　stricter　requirements　for　better　overall　QoS　and　execution　efficiency　of　the　service　chain.　In　this　paper,　we　present　a　data　volume　aware　ant　colony　optimization　approach　called　DVA-MOACO　algorithm　for　geographical　knowledge　cloud　service　composition.　Our　algorithm　utilizes　a　multi-index　service　quality　evaluation　model,　and　improves　the　transition　probability　while　considering　the　data　transfer　cost　and　other　QoS　constraints　simultaneously　when　ant　finding　path.　Our　algorithm　could　reach　the　Pareto　near　optimal　solution　rapidly　with　better　QoS　performance　and　lower　data　transfer　cost　from　numerous　candidate　solutions.　©　2016　SERSC.