Gas load forecasting is an important task for cities to deploy gas safely and economically. At present, the Seq2Seq model based on the attention mechanism is increasingly utilized in gas data forecasting and is an effective method for gas load forecasting. However, the gas load data have such characteristics as high mutation frequency and large amplitude. The Seq2Seq model based on the general attention mechanism is difficult to extract the multivariate time pattern information in the data and deal with data random mutation. It is still necessary for improving gas load prediction with complex influencing factors. Therefore, this study proposes a multi-dimensional attention mechanism Seq2Seq model. On the one hand, a multi-level time attention module is designed and studied to integrate single-time step and multi-time step attention calculation to extract different time pattern information in the data. On the other hand, the design adds a local history attention module. By improving the model’s defect of distinguishing important historical information, the model tends to refer to more important historical information when making predictions. The improved model has better prediction performance for the unique gas load characteristics. The gas consumption data of an urban area in China and the electric load data of the 2016 electrical mathematical modeling competition are taken as examples. The experimental results show that the MAE of the improved model is reduced by 17% and 9% respectively compared with the general attention mechanism Seq2Seq model.