Research　on　the　price　prediction　of　natural　gas　is　of　great　significance　to　market　participants　of　all　kinds.　In　order　to　predict　natural　gas　prices　more　reliably,　this　paper　introduces　a　quadratic　decomposition　technology　based　on　the　combination　of　variational　modal　decomposition　(VMD)　and　ensemble　empirical　modal　decomposition　(EEMD),　which　decomposes　the　residual　term　(Res)　after　VMD　by　EEMD;　then,　a　new　hybrid　model　called　VMD-EEMD-Res.-LSTM　is　constructed　in　combination　with　the　long　short-term　memory　(LSTM)　prediction　model.　The　contribution　of　this　new　hybrid　model　is　that,　unlike　existing　application　research　that　combines　existing　decomposition　technology　with　the　LSTM　model,　it　does　not　ignore　the　important　information　contained　in　the　residual　after　the　VMD.　In　order　to　verify　the　predictive　performance　of　the　proposed　new　model,　this　paper　uses　the　data　of　the　spot　price　of　natural　gas　in　the　United　States　to　conduct　a　multistep-ahead　empirical　comparative　analysis.　The　results　show　that　the　new　hybrid　model　constructed　in　this　paper　has　significant　predictive　advantages.　©　2022　Linjie　Zhan　and　Zhenpeng　Tang.