Interval-valued　data　are　a　special　symbolic　data,　which　contains　rich　information.　The　prediction　of　interval-valued　data　is　a　challenging　task.　In　terms　of　predicting　interval-valued　data,　machine　learning　algorithms　typically　consider　mean　regression,　which　is　sensitive　to　outliers　and　may　lead　to　unreliable　results.　As　an　important　complement　to　mean　regression,　in　this　paper,　a　quantile　regression　artificial　neural　network　based　on　a　center　and　radius　method　(QRANN-CR)　is　proposed　to　address　this　problem.　Numerical　studies　have　been　conducted　to　evaluate　the　proposed　method,　comparing　with　several　traditional　models,　including　the　interval-valued　quantile　regression,　the　center　method,　the　MinMax　method,　and　the　bivariate　center　and　radius　method.　The　simulation　results　demonstrate　that　the　proposed　QRANN-CR　model　is　an　effective　tool　for　predicting　interval-valued　data　with　higher　accuracy　and　is　more　robust　than　the　other　methods.　A　real　data　analysis　is　provided　to　illustrate　the　application　of　QRANN-CR.