In　this　paper,　we　propose　an　effective　method　of　PM2.5　prediction　based　on　image　contrast-sensitive　features　and　weighted　bagging　based　neural　network　(WBBNN).　Three　types　of　image　contrast-sensitive　features　are　first　extracted　from　the　images　and　fuzzified.　Next,　a　weighted　bagging　strategy　combining　the　ensemble　fuzzy　neural　network　(FNN)　and　ensemble　radial　basis　function　neural　network　(RBFNN)　is　established.　The　ensemble　neural　network　(NN),　regardless　of　FNN　and　RBFNN,　is　obtained　by　simply　averaging　the　outputs　of　component　neural　networks.　And　these　component　neural　networks　are　trained　by　the　improved　gradient　descent　algorithm　and　samples　acquired　by　bootstrap　sampling.　Finally,　the　WBBNN　is　used　to　forecast　PM2.5　concentration　by　extracting　three　types　of　image　contrast-sensitive　features.　Results　of　experiments　demonstrate　that　our　prediction　method　is　more　reliable,　practical　and　efficient　than　FNN,　RBFNN,　the　ensemble　NNs,　and　state-of-the-art　quality　assessment　method　in　terms　of　predicting　the　concentration　of　PM2.5　More　importantly,　an　improved　gradient　descent　algorithm　is　developed　to　accelerate　the　convergence　speed　and　ensure　the　prediction　accuracy　of　WBBNN　and　the　fuzzy　features　acquired　by　the　feature　fuzzification　method　can　greatly　improve　the　robustness　and　precision　of　WBBNN.