In view of the nonlinearity and non-stationarity of gas load data, this paper presents a combined forecasting model based on improved WT-LMD and GRU neural networks. Firstly, the model decomposes the gas load data by using the improved LMD algorithm, which improves the over-smoothing problem of that the traditional LMD method uses piecewise Newton interpolation instead of the traditional sliding average method to obtain local mean function and envelope estimation function. After that, the PF components are processed with wavelet threshold denoising to obtain effective component data. Finally, The GRU neural network is used to predict the value of each component separately, and the final predicted value of load is obtained by adding them. Simulation results show that the proposed method is more accurate than single GRU neural network and GRU network combined with traditional LMD algorithm.