A short term electricity price prediction method based on variational mode decomposition and hybrid deep neural network is proposed to address the characteristics of nonlinearity, volatility, and timeliness in electricity price data in the electricity market. Firstly, the original electricity price sequence is decomposed into multiple stationary subsequences using variational mode decomposition (VMD). Secondly, a hybrid deep neural network prediction model is used to predict and superimpose each subsequence separately, obtaining the final electricity price prediction result. This model combines convolutional neural network (CNN) and bidirectional long short term memory (BiLSTM) network to effectively extract spatial and temporal features of the original electricity price data, and combines attention mechanism to effectively distinguish the importance of electricity price data at different times in the original electricity price sequence. Finally, simulation analysis is conducted using actual electricity price data from the PJM electricity market in the United States, and the effectiveness of the proposed method is verified by comparing multiple electricity price prediction models.