Packet　classification　is　an　essential　part　of　computer　networks.　Existing　algorithms　propose　a　partition　process　to　address　the　memory　explosion　problem　of　the　decision　tree　algorithm　caused　by　the　huge　number　of　rules　with　multiple　fields.　However,　the　search　process　requires　traversing　multiple　trees　generated　by　the　partition,　which　reduces　the　search　efficiency.　The　existing　algorithms　take　simple　approaches　to　optimize　the　search　process,　which　is　low　efficiency　or　high　hardware　overhead.　In　this　paper,　we　propose　DTRadar,　a　framework　for　expediting　the　decision　tree　packet　lookup　process.　Its　key　idea　is　building　an　abstract　One-Big-Tree(OBT)　for　multiple　decision　trees　by　establishing　the　middle　data　structure.　DTRadar　considers　each　decision　tree　as　a　splittable　tree　and　organizes　these　subtrees　by　intermediate　data　structures.　Extensive　experiments　show　that　DTRadar　benefits　existing　decision　tree-based　solutions　in　classification　time　by　61.60%,　and　the　memory　footprint　only　increased　by　4.21%　on　average.　©　2023　IEEE.