The　functional　linear　quantile　regression　model　is　widely　used　to　characterize　the　relationship　between　a　scalar　response　and　a　functional　covariate.　Most　existing　research　results　are　based　on　a　correct　assumption　that　the　response　is　related　to　the　functional　predictor　through　a　linear　model　for　given　quantile　levels.　This　paper　focuses　on　investigating　the　adequacy　check　of　the　functional　linear　quantile　regression　model.　We　propose　a　nonparametric　U-process　test　statistic　based　on　the　functional　principal　component　analysis.　It　is　proved　that　the　test　statistic　follows　a　normal　distribution　asymptotically　under　the　null　hypothesis　and　diverges　to　infinity　for　any　misspecified　models.　Therefore,　the　test　is　consistent　against　any　fixed　alternative.　Moreover,　it　is　shown　that　the　test　has　asymptotic　power　one　for　the,　local　alternative　hypothetical　models　converging　to　the　null　hypothesis　at　the　rates　n(-1/2).　The　finite　sample　properties　of　the　test　statistic　are　illustrated　through　extensive　simulation　studies.　A　real　data　set　of　24　hourly　measurements　of　ozone　levels　in　Sacramento,　California　is　analyzed　by　the　proposed　test.　(C)　2020　Elsevier　B.V.　All　rights　reserved.