The　application　of　continuous-time　quantum　walk　in　the　field　of　image　segmentation　has　attracted　much　attention　due　to　the　advantages　of　quantum　computation.　However,　the　proposed　image　segmentation　algorithm　constructs　the　continuous-time　quantum　walk　model　based　on　pixels,　which　will　cause　a　huge　burden　on　quantum　resources,　and　the　various　feature　information　of　the　image　cannot　be　better　considered.　In　addition,　this　pixel-based　processing　method　requires　a　lot　of　manual　annotation　to　achieve　the　desirable　segmentation　effect.　To　address　these　issues,　we　propose　an　image　segmentation　algorithm　using　continuous-time　quantum　walk　based　on　superpixels.　In　our　segmentation　algorithm,　the　original　image　is　firstly　segmented　into　superpixels,　and　then　a　weighted　graph　is　constructed　with　superpixels　as　nodes,　where　the　weight　of　edges　in　graph　is　measured　by　the　feature　similarity　of　two　adjacent　superpixels,　which　consists　of　color　features　and　texture　features.　Next,　the　continuous-time　quantum　walk　model　is　constructed　based　on　the　weighted　graph　by　redefining　the　new　Hamiltonian　operator.　Finally,　continuous-time　quantum　walk　is　executed　and　the　image　segmentation　result　can　be　obtained,　which　is　realized　by　assigning　each　superpixel　the　class　label　corresponding　to　the　greatest　probability.　Experiments　on　the　BSD500　dataset　show　that　the　proposed　algorithm　can　significantly　improve　segmentation　efficiency　and　accuracy　while　the　manually　selected　seeds　is　reduced　by　91%.　More　importantly,　the　new　algorithm　reduce　the　demision　of　the　quantum　walk　system　by　more　than　99%,　which　will　yield　a　huge　saving　on　the　quantum　resources.