With　the　rapid　development　of　urbanization,　environmental　pollution　has　drawn　worldwide　attention.　Accurate　air　quality　prediction　is　very　significant　for　alleviating　severe　pollution　conditions　and　human　healthy　life.　A　novel　hybrid　model　that　combines　a　spatiotemporal　correlation　analysis　method　and　an　effectively　simple　network　is　proposed　to　forecast　the　concentrations　of　air　pollutants.　Firstly,　a　new　calculation　method　considering　distance　and　PM2.5　concentrations　among　stations　is　proposed　to　select　stations　strongly　correlated　with　the　target　station　on　the　basis　of　grey　relation　analysis.　Secondly,　a　fully　convolutional　network　is　proposed　to　extract　spatiotemporal　features　efficiently　based　on　a　temporal　convolutional　network.　In　addition,　meteorological　factors　and　other　pollutants　are　selected　as　auxiliary　factors　to　further　improve　the　prediction　accuracy.　To　verify　the　validity　of　the　proposed　model,　the　air　quality　and　meteorological　data　collected　from　40　monitoring　stations　in　Fushun,　which　is　an　important　industrial　city　in　China,　are　applied　for　air　quality　prediction.　The　performance　of　models　is　evaluated　by　a　series　of　metrics.　The　RMSE,　MAE,　and　R-2　values　of　the　proposed　model　are　12.505,　8.214,　and　0.884　for　forecasting　the　next　hour　PM2.5　concentration.　Compared　with　other　conventional　models　and　hybrid　models,　the　proposed　model　verified　that　it　outperforms　the　other　models　with　air　quality　prediction.