Short-term load is influenced by multiple external factors and shows strong nonlinearity and volatility, which increases the forecasting difficulty. However, most of existing short-term load forecasting methods rely solely on the original load data or take into account a single external factor, which results in significant forecasting errors. To improve the forecasting accuracy, this paper proposes a short-term load forecasting method considering multiple contributing factors based on VAR and CEEMDAN-CNN- BILSTM. Firstly, multiple contributing factors strongly correlated with the short-term load are selected based on the Spearman correlation analysis, the vector autoregressive (VAR) model with multivariate input is derived, and the Levenberg–Marquardt algorithm is introduced to estimate the model parameters. Secondly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm and permutation entropy (PE) criterion are combined to decompose and reconstruct the original load data into multiple relatively stationary mode components, which are respectively input into the CNN-BILTSM network for forecasting. Finally, the sine–cosine and Cauchy mutation sparrow search algorithm (SCSSA) is used to optimize the parameters of the combinative model to improve the forecasting accuracy. The actual simulation results utilizing the Australian data validate the forecasting accuracy of the proposed model, achieving reduction in the root mean square error by 31.21% and 18.04% compared to the VAR and CEEMDAN-CNN-BILSTM, respectively.