Electricity　price　prediction　is　essential　for　the　optimal　dispatch　in　power　markets,　with　accurate　prediction　models　being　critical　for　efficient　power　system　operations　and　market　trading　decisions.　Deep　learning　networks,　with　their　powerful　nonlinear　modeling　capabilities,　have　shown　promising　results　in　electricity　price　forecasting.　However,　their　design　techniques,　especially　the　selection　of　network　parameters,　remain　challenging.　This　indicates　that　the　optimization　and　exploration　of　deep　learning　networks　in　electricity　price　forecasting　models　require　further　investigation.　This　paper　innovatively　proposes　a　forecasting　model　that　uniquely　integrates　Variational　Mode　Decomposition　(VMD),　Grey　Wolf　Optimization　(GWO),　Attention　Mechanism　(ATT),　and　Long　Short-Term　Memory　Network　(LSTM),　optimizing　the　model　from　three　different　perspectives.　First,　during　the　data　preprocessing　phase,　the　training　set　is　subjected　to　VMD　to　reduce　noise,　thereby　enhancing　the　capture　of　multi-scale　characteristics　inherent　in　electricity　price　time　series.　The　ATT　layer　is　integrated　to　adaptively　allocate　weights,　enhancing　the　model＇s　focus　on　significant　features.　The　GWO　is　applied　to　optimize　hyperparameters　of　the　LSTM,　accelerating　convergence　and　improving　iteration　accuracy,　thereby　reducing　model　error.　A　series　of　experiments　were　conducted　using　multiple　regional　electricity　price　datasets,　evaluated　with　several　metrics　including　RMSE.　The　results　validated　the　effectiveness　of　the　proposed　three　modules　in　improving　the　performance　of　the　time　series　network,　with　VMD　making　the　most　significant　contribution.　Among　all　models,　VMD-GWO-ATT-LSTM　consistently　outperformed　others,　demonstrating　its　effectiveness　and　robustness　in　electricity　price　forecasting.