Natural gas demand forecasting is recognized as a critical issue for natural gas production systems, pipeline management and operational systems, and downstream market management. The combination of data decomposition algorithms with time series forecasting models was commonly adopted to extract features from raw data and improve prediction accuracy. However, existing models usually integrate decomposed sequence data directly, which prevents the full potential of decomposition algorithms from being realized. A novel forecasting model integrating singular spectrum analysis (SSA) with an improved time series mixer (TsMixer), termed SSA-TsMixer, was proposed. The feature extraction structure of the TsMixer model was innovatively redesigned. By allocating decomposed sub-series appropriately across different feature extraction layers, effective interaction among sequences was achieved. This enables the unified features abstracted by the decomposition algorithm to be fully utilized, while the interference of noise data was suppressed. Extensive empirical studies were conducted on gas consumption data from 12 end-users. The model was evaluated on all test sets for forecasting horizons from 1 step to 14 steps. Superior performance in accuracy and stability is demonstrated. The mean absolute percentage error (MAPE) ranges from 3.39% to 7.57%, which corresponds to a 13.65% error reduction compared to the original model. Furthermore, ablation experiments at a 1-step horizon were carried out. The effectiveness of the designed multi-sequence feature extraction structure was verified. The SSA-TsMixer model enhances forecasting accuracy and stability. It provides effective technical support for the intelligent scheduling and control of natural gas pipeline networks, and has significant engineering value.