With　wind　power　technology　becoming　more　sophisticated,　the　scale　of　power　generation　capacity　and　grid-connected　wind　power　facilities　have　been　greatly　expanded,　and　the　power　of　wind　power　plants　has　increased,　penetrating　a　series　of　problems　here.　System　supplies　power　to　the　increasingly　prominent,　the　stable　operation　of　power　system　and　seriously　threatens.　To　better　arrange　dispatch,　it　is　especially　important　to　make　the　wind　power　prediction　more　precise.　We　propose　a　hybrid　model　wind　power　prediction　method　based　on　CNN　(convolutional　Neural　Network)　and　LSTM　(Long　term　Memory　Network)　for　time　series　and　nonlinear　characteristics　of　power　data.　Firstly　input　large-volume　historical　wind　data,　relevant　influence　factor　data　and　date　information　according　to　time-sliding　window,　in　which　a　continuous　feature　map　is　built.　The　above　data　extracted　and　normalized　feature　vectors　of　the　data　through　MULTI-layer　convolution　and　pooling　stacking　through　CNN.　The　obtained　feature　vectors　are　used　as　input　data　of　the　LSTM　network　in　the　form　of　time　series,　through　the　LSTM　network　for　wind　power　prediction.　By　using　the　above　method,　the　data　set　HDWF2　is　selected　for　prediction　experimental　data　information　in　this　paper　including　cloud　cover,　dew　point,　humidity,　ozone,　precipitation　intensity,　pressure,　temperature,　UV　index,　visibility,　wind　bearing,　gust,　wind　speed,　and　power.　Experiments　show　that　the　method　brought　up　in　the　paper　can　predict　more　precisely　than　BP　network　wind　power　prediction　method　and　standard　LSTM　network　wind　power　prediction　method,　and　the　R2　value　can　reach　0.9026.　©　2023　IEEE.