Abstract: This paper proposes a short-term power load forecasting method based on hybrid convolutional neural networks to address the challenges of accuracy,stability,and adaptability to environmental factors in load forecasting tasks.First of all,A multi-scale feature fusion method based on 1DCNN（1D convolutional neural network,1D-CNN）is proposed,which captures the trend of load changes by fusing features of different scales, improving the recognition ability of load mutations and complex patterns;A multi feature factor learning method based on 2D-CNN is designed to address the impact of various environmental characteristic factors on electricity loads,which improves the modeling ability of the model for complex relationships between environmental factors and loads. Second,a hybrid network model is constructed to achieve a comprehensive load forecasting method that effectively associates spatiotemporal features through deep feature fusion and information propagation of 1D-CNN and 2D-CNN feature information. Specific case studies are conducted to analyze the impact of parameter optimization and fusion learning on model accuracy and efficiency,and compared with classical models.The results show that the root mean squared error（RMSE）value of the model is 36.3,while the mean absolute error value is5.34,and the mean absolute percentage error（MAPE）value is1.02%,effectively improving the accuracy and robustness of the load forecasting.