This　paper　is　focused　on　the　goodness-of-fit　test　of　the　functional　linear　composite　quantile　regression　model.　A　nonparametric　test　is　proposed　by　using　the　orthogonality　of　the　residual　and　its　conditional　expectation　under　the　null　model.　The　proposed　test　statistic　has　an　asymptotic　standard　normal　distribution　under　the　null　hypothesis,　and　tends　to　infinity　in　probability　under　the　alternative　hypothesis,　which　implies　the　consistency　of　the　test.　Furthermore,　it　is　proved　that　the　test　statistic　converges　to　a　normal　distribution　with　nonzero　mean　under　a　local　alternative　hypothesis.　Extensive　simulations　are　reported,　and　the　results　show　that　the　proposed　test　has　proper　sizes　and　is　sensitive　to　the　considered　model　discrepancies.　The　proposed　methods　are　also　applied　to　two　real　datasets.