Various　stages　of　highway　project　construction　process　involve　text,　image,　audio,　video　and　other　related　data　sources　involving　many　participants,　forming　a　huge　amount　of　data.　Accurately　tracing　the　source　of　responsibility,　refining　and　applying　the　unbalanced　data　in　the　highway　project　archives　is　of　great　significance　for　realizing　the　intelligent　transformation　of　highway　construction　project　management.　This　paper　firstly　sorts　out　the　construction　process　of　highway　projects　and　the　main　data　sources,　constructs　a　data　association　network　between　construction　entities　and　construction　process,　as　well　as　a　knowledge　map　of　highway　construction　data.　Then,　according　to　the　highway　construction　stage,　an　index　system　based　on　12　key　data　is　constructed　by　using　the　entropy　weight　cloud　model　method,　and　the　importance　of　the　data　is　evaluated.　Thirdly,　based　on　the　unbalanced　characteristics　of　highway　project　data,　a　method　of　mining　big　data　in　highway　project　archives　using　classification　evaluation　indexes　is　proposed,　and　the　accuracy　of　this　method　is　verified　by　case　calculation.　Finally,　taking　the　Shizong　Qiubei　Expressway　in　China　as　an　example,　the　intelligent　management　and　control　suggestions　for　key　data　of　transportation　projects　are　proposed.　It　is　found　that　the　key　data　with　special　importance　rate　in　highway　construction　include　construction　data,　supervision　data　and　completion　data.　Boosting　algorithm　is　more　accurate　than　the　traditional　SMOTE　algorithm　for　unbalanced　data　mining,　which　helps　to　save　the　project　construction　cost　and　improve　the　quality　of　data　extraction　in　the　project　archives.　This　study　provides　a　theoretical　reference　for　key　data　traceability　of　highway　project　intelligent　management　and　control　platform　and　the　improvement　of　intelligent　management　efficiency.　©　2023