This study proposes a CEEMDAN-SBiGRU combined prediction model with an optimized multi-head attention mechanism to enhance the precision of short-term power load forecasting and fully explore the complex correlation of power load data. The model improves two modules: feature extraction and feature fusion. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is utilized to decompose the power load data into multiple intrinsic mode function (IMF) and a residual signal (RES); and a denoising autoencoder DAE is introduced to extract potential features from the data affected by meteorological factors, workday types, and temperature changes. Secondly, the extracted intricate features are fed into the stacked bidirectional gated recurrent unit (SBiGRU) module to obtain hidden states. Finally, the obtained hidden states are input into the optimized multi-head attention (OMHA) mechanism module,which incorporates residual mechanism and layer normalization, to accurately assign higher weights to important features and solve the problem of noise interference. The experimental results indicate that the CEEMDAN-SBiGRU-OMHA combined model achieves higher accuracy.