Advanced　wind　power　prediction　technique　plays　an　essential　role　in　the　stable　operation　of　the　grid　with　largescale　grid　integration　of　wind　power.　Most　research　focuses　on　distance-based　static　classification　where　the　subjective　nature　of　initial　center　selection　increases　the　uncertainty　of　the　prediction.　And　the　data　classification　on　a　daily　basis　neglects　the　potentially　significant　climate　changes　at　smaller　time　scales.　To　address　these　issues,　the　improved　snake　optimization-long　short-term　memory　(ISO-LSTM)　model　with　Gaussian　mixture　model　(GMM)　clustering　is　proposed　to　forecast　wind　power　from　an　adaptive　perspective.　By　exploiting　the　merits　of　the　probabilistic　classification,　the　K-means　optimized　GMM　clustering　enables　an　appropriate　feature　modelling　for　substantial　climate　changes　at　smaller　time　scales.　Then　the　ISO　algorithm　exhibits　higher　search　accuracy　and　is　better　suited　for　finding　hyperparameter　combinations　for　LSTM　neural　networks.　The　data　from　the　National　Aeronautics　and　Space　Administration　(NASA)　of　the　US　is　used　to　validate　the　effectiveness　of　the　proposed　method.　Compared　to　the　traditional　K-means　clustering,　the　K-means　optimized　GMM　clustering　has　increased　accuracy　by　2.63　%.　Simultaneously,　with　the　adoption　of　the　enhanced　ISO　algorithm,　the　accuracy　further　increases　by　7.27　%.　Different　existing　models　have　also　been　tested;　it　shows　that　the　proposed　model　demonstrates　higher　prediction　accuracy.