The　technical　condition　of　road　pavements　requires　regular　monitoring　to　support　accurate　maintenance　decisions.　However,　existing　pavement　maintenance　quality　index　(PQI)　assessment　methods　are　often　expensive　and　infrequent,　which　limits　intelligent　decision-making.　This　paper　proposes　a　cost-effective　and　precise　PQI　assessment　method　using　data　from　five　highways　in　Beijing.　Key　PQI　indicators　were　identified　through　a　hybrid　feature　selection　method,　and　a　CatBoost　model　optimised　by　a　genetic　algorithm　(GA-CatBoost)　was　developed.　The　model　demonstrated　superior　accuracy,　achieving　an　R2　of　0.938,　an　MSE　of　0.838,　and　an　MAE　of　0.576.　It　achieved　98.46%　accuracy　compared　to　traditional　methods,　confirming　its　reliability　in　an　on-site　application.　This　approach　offers　an　economical　solution　for　high-frequency　PQI　assessment,　enabling　intelligent　decision-making　in　road　infrastructure　maintenance　and　supporting　the　use　of　lightweight　detection　equipment.