To　make　up　the　poor　efficiency　and　low　prediction　accuracy　of　traditional　prediction　methods　in　the　pavement　performance　prediction　problem,　this　study　aimed　at　the　prediction　of　the　pavement　condition　index　(PCI)　of　the　ordinary　highway　asphalt　pavement,　and　then　proposed　a　PCI　prediction　model　based　on　random　forest　algorithm　by　using　the　performance　indexes,　pavement　structure,　traffic　parameters　and　meteorological　data　of　1　249　group　observations　of　9　counties　in　Beijing.　The　prediction　results　were　compared　with　the　neural　network　and　support　vector　machine　models.　Results　show　that　by　comparing　and　analyzing　three　quantitative　evaluation　indexes　(root　mean　square　error,　mean　absolute　error　and　r-square)　of　different　models　and　visual　scatter　plots,　the　robustness　and　accuracy　of　the　PCI　prediction　model　using　random　forest　algorithm　are　superior　to　the　neural　network　and　the　support　vector　machine　models.　It　can　provide　scientific　basis　for　subsequent　highway　maintenance　budget　application　and　decision-making　plan　formulation,　which　is　of　great　significance　for　improving　the　economic　benefits　of　highway　maintenance.　©　2021,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.