This　paper　investigates　the　hypothesis　test　of　the　parametric　component　in　partial　functional　linear　quantile　regression　model　in　which　the　dependent　variable　is　related　to　both　a　vector　of　finite　length　and　a　function-valued　random　variable　as　predictor　variables.　A　quantile　rank　score　test　based　on　functional　principal　component　analysis　is　developed.　Under　mild　conditions,　we　establish　the　consistency　of　the　proposed　test　statistic,　and　show　that　the　proposed　test　can　detect　Pitman　local　alternatives　converging　to　the　null　hypothesis　at　the　usual　parametric　rate.　A　simulation　study　shows　that　the　proposed　test　procedure　has　good　size　and　power　with　finite　sample　sizes.　Finally,　an　illustrative　example　is　given　through　fitting　the　Berkeley　growth　data　and　testing　the　effect　of　gender　on　the　height　of　kids.