To　further　improve　the　performance　of　image　retrieval,　we　propose　an　image　two-classification　algorithm　based　on　continuous-time　quantum　walk　(CTQW)　model.　First,　a　weighted　undirected　complete　graph　is　constructed　based　on　a　new　weighted　edge　method,　that　is,　the　images　in　the　image　set　are　used　to　represent　the　nodes　on　the　weighted　complete　graph,　and　every　two　images　are　connected　to　represent　the　edge　on　the　weighted　complete　image,　the　weighted　value　of　which　represents　the　similarity　between　adjacent　images.　Then,　the　CTQW　is　executed　to　realize　the　image　two-classification　based　on　the　quantum　walk　model　of　the　weighted　complete　graph,　that　is,　the　CTQW　is　performed　from　two　types　of　unrelated　images,　and　the　two-classification　of　images　is　realized　that　the　relevant　and　unrelated　labels　are　assigned　to　each　image　according　to　the　obtained　probability　distribution　two-classification.　Finally,　the　experimental　results　show　that　the　proposed　two-classification　algorithm　based　on　CTQW　has　a　good　classification　effect　from　the　visual　effect,　and　the　evaluation　indexes　of　image　classification,　such　as　the　average　precision　rate,　the　recall　rate,　and　the　accuracy　rate,　are　all　higher　than　0.85.　In　addition,　the　proposed　image　two-classification　algorithm　based　on　CTQW　can　be　extended　to　image　multiclassification　if　the　quantum　walk　is　performed　using　multiple　irrelevant　pictures　as　the　starting　point.　Similarly,　the　extended　image　multiclassification　method　also　has　a　better　classification　effect　through　the　experiments　and　the　evaluation　indexes　of　image　classification.