The gas load is affected by a variety of factors, which cause the trend of gas load changes to have greater complexity and more redundancy of eigenfactors. In order to solve this problem, the EEMD adaptive time-frequency localization analysis method is used to deal with the complexity of the gas load. The non-stationary gas load data is decomposed into stationary eigenmode components and residual terms. Mutual information analysis is added to the PCA to select the eigenvectors. The relationship between the features and the gas load value can be considered in the dimension reduction. The corresponding LSTM model outputs the final result. Using Shanghai gas data for verification, the experimental results prove that the method proposed in this study has a MAPE of 6.36%, which is lower than the error of other models.