For　the　severe　haze　situation　in　the　Beijing-Tianjin-Hebei　region,　conventional　fine　particulate　matter　(PM2.5)　concentration　prediction　methods　based　on　pollutant　data　face　problems　such　as　incomplete　data,　which　may　lead　to　poor　prediction　performance.　Therefore,　this　paper　proposes　a　method　of　predicting　the　PM2.5　concentration　based　on　image　analysis　technology　that　combines　image　data,　which　can　reflect　the　original　weather　conditions,　with　currently　popular　machine　learning　methods.　First,　based　on　local　parameter　estimation,　autoregressive　(AR)　model　analysis　and　local　estimation　of　the　increase　in　image　blur,　we　extract　features　from　the　weather　images　using　an　approach　inspired　by　free　energy　and　a　no-reference　robust　metric　model.　Next,　we　compare　the　coefficient　energy　and　contrast　difference　of　each　pixel　in　the　AR　model　and　then　use　the　percentages　to　calculate　the　image　sharpness　to　derive　the　overall　mass　fraction.　Furthermore,　the　results　are　compared.　The　relationship　between　residual　value　and　PM2.5　concentration　is　fitted　by　generalized　Gauss　distribution　(GGD)　model.　Finally,　nonlinear　mapping　is　performed　via　the　wavelet　neural　network　(WNN)　method　to　obtain　the　PM2.5　concentration.　Experimental　results　obtained　on　real　data　show　that　the　proposed　method　offers　an　improved　prediction　accuracy　and　lower　root　mean　square　error　(RMSE).