To tackle the complexity and nonlinearity inherent in natural gas load sequences,this paper proposes a combined forecasting model that integrates Time2Vec,LSTM (Long Short-Term Memory),TCN (Temporal Convolutional Network),and attention mechanism.Initially,the Pearson correlation coefficient is used to conduct the correlation analysis to extract the meteorological features that exhibit strong relevance.Subsequently,the time vector embedding layer of Time2vec is introduced to convert the time series data into a continuous vector space,thus enhancing the model's computational efficiency in processing time series information.Then the temporal features extracted by Time2Vec,alongside the meteorological features selected using Pearson correlation coefficient,are fed into both the LSTM and TCN models for prediction,exploiting the long-term memory capability of LSTM and the local feature extraction capability of TCN.Finally,these two models are combined through attention mechanism,and assigned different weights according to the importance of the two to obtain the final prediction results.The experimental results show that the proposed Time2Vec-LSTM-TCN-Attention model outperforms other combined models in terms of adaptability and accuracy for natural gas load forecasting.