To　improve　the　predictive　ability　of　existing　models　for　predicting　the　compaction　density　of　asphalt　pavement，a　test　site　was　set　up　on　the　upper　layer　of　the　Xiang’an　Airport　Highway　Project　in　Xiamen.　The　CCV，DMV　and　VCV，which　represent　the　change　of　harmonic　ratio，energy，and　mechanics　in　the　vibration　and　compression　process，respectively，as　well　as　the　temperature，were　chosen　as　indicators.　The　isolation　forest　algorithm　was　used　to　detect　outliers　in　indicators.　The　density　prediction　model　was　established　based　on　the　partial　least　squares　regression.　The　results　show　that　the　isolation　forest　can　effectively　recognize　outliers　of　high-dimensional　data，covering　the　shortage　that　traditional　methods　can　only　process　one-dimensional　data.　There　are　different　degrees　of　positive　correlation　between　temperature，other　indicators，and　asphalt　pavement　density.　The　multiple　regression　model　based　on　CCV，DMV，and　VCV　obtains　better　fitting　ability　than　the　unitary　regression　methods，proving　the　feasibility　of　multiple　indicators.　The　partial　least　squares　regression　can　restrain　the　adverse　impact　caused　by　the　approximate　collinearity　between　independent　variables，correct　the　incorrect　weight　of　temperature，and　improve　the　fitting　degree　compared　with　the　common　multiple　linear　regression　methods.　The　final　determination　coefficient　of　the　model　on　the　training　set　is　0.83，and　on　the　test　set　is　0.81，indicating　good　predictive　ability　for　asphalt　pavement　density.　©　2024　Hunan　University.　All　rights　reserved.