The　accuracy　of　short-term　electricity　price　forecasting　has　profound　influence　on　the　business　decisions　made　by　the　participants　in　the　electricity　market.　Thus,　we　propose　a　short-term　electricity　price　combination　prediction　model　based　on　Complete　Ensemble　Empirical　Mode　Decomposition(CEEMD)　as　well　as　Temporal　Convolutional　Attention-based　Network(TCAN).　First,　we　defined　the　correlations　among　the　features　according　to　the　Pearson　Correlation　Analysis　and　selected　the　desired　features　for　our　model.　We　then　defined　the　single　prediction　period　to　be　24　hours　based　on　the　Unit　Root　test.　In　addition,　we　decomposed　and　denoised　the　data　based　on　CEEMD　and　built　the　attention　mechanism　into　the　temporal　convolution　network.　Using　the　expansion　cascade　network　to　extract　the　relationship　between　the　inputs,　combined　with　the　self-attention　mechanism　to　extract　the　internal　information　and　learn　the　position　dependence,　effectively　improve　the　model　training　speed　and　prediction　accuracy.　In　order　to　verify　the　validity　of　the　model,　the　actual　electricity　price　data　of　DK1　in　the　Nordic　electricity　market　from　April　to　May　2020　were　forecasted.　The　results　show　that　the　proposed　method　can　effectively　improve　the　accuracy　and　reliability　of　electricity　price　forecasting,　and　can　provide　reliable　decision-making　basis.　©　2022　IEEE.