With　the　progress　of　data-driven　materials　design,　the　scale　and　quality　of　datasets　for　intelligence　algorithms　such　as　machine　learning　and　data　mining　have　actually　become　the　bottleneck　for　the　practical　benefit.　The　massive　experimental　data　reported　in　the　literatures　and　those　unpublished　are　significant　treasure　trove　for　producing　high-quality　datasets.　However,　there　has　been　a　lack　of　specific　database　or　information　management　system　for　storing　and　managing　data　as　well　as　generating　high-quality　datasets.　Using　the　Sm-Co　alloy　system　as　an　example,　which　is　a　typical　representative　of　the　high-temperature　permanent　magnetic　materials,　we　established　an　intelligent　database　and　the　corresponding　information　management　system,　where　the　data　acquisition,　data　annotation,　data　extraction　and　data　format　conversion　are　flexibly　integrated.　We　set　up　correlation　models　for　the　elemental　composition,　phase　constitution,　crystal　structure,　processing,　measurement,　and　properties　of　the　materials.　From　these　models,　the　collected　data　are　structured　and　the　logical　associations　of　related　data　are　explicit.　Thus,　high-quality　datasets　can　be　produced　from　the　developed　system　according　to　certain　requirements　for　materials　design.　In　the　developed　information　management　system,　the　online　data　annotation　by　multi-user　has　been　realized.　Moreover,　the　system　is　advanced　in　high-efficiency　data　entry　and　unifying　data　format,　and　precise　search　for　redundant　data.　It　has　been　confirmed　that　the　present　specific　database　and　information　management　system　play　an　important　role　in　the　data-driven　materials　design,　and　are　promising　in　the　applications　in　the　fields　of　materials　genome　initiative　and　materials　informatics.　©　2020,　Science　Press.　All　right　reserved.