Aiming at the complexity and nonlinearity of natural gas load sequence, this paper proposes a natural gas load combination forecasting model based on Time2Vec. Firstly, Time2vec, a time vector embedding layer, is introduced to convert the time series into a continuous vector space, which improves the computational efficiency of the model on the time series information; secondly, the Pearson correlation coefficient is used for the correlation analysis, and the meteorological features with strong correlation are extracted as inputs. The temporal features extracted by Time2Vec and the meteorological features selected by Pearson"s correlation coefficient are inputted into the two models of Long Short-Term Memory Network (LSTM) and Temporal Convolutional Network (TCN) for prediction, which make full use of the long term memory capability of LSTM and the local feature extraction capability of TCN. Finally, these two models are combined through Attention mechanism (Attention), and different weights are assigned according to the importance of the two to obtain the final prediction results. The experimental results show that the proposed Time2Vec-based natural gas load combination prediction model has stronger adaptability and higher accuracy.