Daily　PM2.5　level　has　significant　influence　on　human　health,　which　is　attracting　increasing　attention.　The　prediction　of　PM2.5　grades　has　thus　become　an　important　factor　closely　related　to　social　development.　In　the　past　decades,　many　prediction　methodologies　for　PM2.5　have　been　developed,　including　regression　analysis,　neural　network　model,　and　support　vector　machine　model.　Despite　these　progresses,　it　still　remains　a　great　challenge　to　predict　the　PM2.5　grades　more　accurately　and　efficiently.　In　this　work,　we　applied　meteorological　pattern　analysis　to　assist　the　support　vector　machine　(SVM)　model　for　PM2.5　class　prediction.　Cosine　similarity　was　first　used　to　extract　three　most　relevant　ones　from　six　common　meteorological　parameters　(atmospheric　pressure,　relative　humidity,　air　temperature,　wind　speed,　wind　direction,　cumulative　precipitation)　to　give　the　needed　meteorological　pattern　for　SVM　model.　Higher　prediction　accuracy　was　then　obtained　with　the　selected　pattern　composed　by　relative　humidity,　wind　speed　and　wind　direction.　Moreover,　genetic　algorithm　(GA)　and　particle　swarm　optimization　(PSO)　algorithm　were　investigated　for　optimizing　the　parameters　in　the　process　of　SVM　classification,　with　PSO-SVM　presenting　the　highest　accuracy　and　efficiency　(forecasting　time　significantly　reduced　by　25%).　We　further　introduced　the　criteria　of　precision,　recall　and　F1-score　to　evaluate　the　prediction　results　of　PSO-SVM　in　each　PM2.5　grade.　Meanwhile,　comparative　studies　confirmed　that　PSO-SVM　displayed　better　performance　than　Adaboost　and　ANN　models　for　the　applied　meteorological　pattern　analysis　assisted　PM2.5　grades　prediction.　These　obtained　results　indicate　the　validity　of　meteorological　pattern　analysis　for　efficient　air　quality　forecasting.