To　solve　the　training　efficiency　and　accuracy　bottleneck　problems　of　the　traditional　neural　network　method　in　modeling　and　identification　application　of　complex　systems,　an　adapted　wavelet　neural　network　(AWNN)　method　was　proposed.　First,　adapted　and　integrated　layers　were　design　to　make　AWNN　create　normalization　parameter　to　adapt　the　sample　data.　AWNN　absorbed　the　advantages　of　BP　neural　network,　RBF　neural　network　and　wavelet　analysis　algorithm　overcome　the　problems　of　the　original　neural　network.　A　large　number　of　experiments　and　comparative　analysis　had　been　implemented　to　verify　the　performance　and　characteristics　of　the　AWNN.　Both　computer　simulation　results　and　intelligent　video　analysis　application　experiments　show　that　the　AWNN　method　has　faster　convergence　speed,　higher　accuracy　and　better　robustness.