Electricity　price　forecasting　(EPF)　is　crucial　for　the　optimal　dispatch　of　energy　markets.　The　increasing　penetration　of　renewable　energy　for　electricity　generation　has　added　more　influencing　variables　to　the　electricity　price　curve,　making　the　EPF　more　challenging.　Therefore,　this　paper　addresses　electricity　price　data　in　energy　markets　with　renewable　energy　generation　and　proposes　an　innovative　Variational　Mode　Decomposition　(VMD)based　multi-attention　mechanism　feature　fusion　model　(V-MAF)　for　EPF.　First,　VMD　processing　reduces　noise　and　captures　multi-scale　features　in　price　and　load　sequences.　Next,　by　integrating　Gated　Recurrent　Units　(GRU),　Temporal　Convolutional　Networks　(TCN),　and　Squeeze-and-Excitation　Networks　(SENet),　a　parallel　network　architecture　combining　SE-TCN　and　SE-GRU　is　constructed.　This　architecture　captures　local　fluctuations　and　periodic　patterns　in　VMD-separated　multi-scale　data,　enhancing　feature　exploration　and　improving　the　model＇s　ability　to　fit　price　variations.　Finally,　the　output　features　from　both　networks　are　combined　and　fed　into　a　Multi-Head　Attention　(MHA)　along　with　the　original　features,　allowing　the　model　to　focus　on　different　parts　of　the　input　features　from　multiple　perspectives.　The　innovative　architecture　enhances　the　ability　to　capture　multi-scale　features　in　time　series　and　further　focuses　on　key　features　through　adaptive　weight　allocation　of　the　attention　mechanism.　Experiments　on　the　Singapore　dataset　and　ablation　studies　demonstrated　the　effectiveness　of　VMD,　SENet,　and　MHA　in　enhancing　network　performance.　Multi-model　comparisons　showed　that　the　V-MAF　model　outperformed　others,　providing　more　stable　and　accurate　predictions.　On　Dataset　1,　the　V-MAF　model　achieved　the　Root　Mean　Square　Error　(RMSE)　of　1.3168,　reduced　errors　by　11.09%　to　59.13%　compared　to　other　models　such　as　XGBoost,　ATT-CNN-LSTM,　BiGRU,　and　VMD-Transformer.