Research on Macroeconomic Nonlinear Forecasting Based on DCL-MHA Collaborative Architecture and Residual Gating Mechanism

To address non-linear fluctuations and error accumulation in macroeconomic forecasting, this paper proposes the DCL-MHA framework, a dual-track architecture integrating an LSTM_Temporal_Model with a Residual Gating chain. By simulating econometric error correction logic, this design enables context-aware dynamic weighting and high-fidelity feature preservation across multi-dimensional economic indicators. Empirical research conducted on 2021–2024 GDP data demonstrates that the proposed model achieves a substantial breakthrough in accuracy with a 1.2% MAPE, representing a 50% improvement over traditional ARIMA, VAR, and standalone LSTM models. Furthermore, in T+12 long-range stress tests, the framework successfully suppressed the RMSE from over 8.0 to approximately 4.0, effectively doubling the forecasting stability. Heatmap analysis of dynamic weights further confirms that the Residual Gating mechanism adaptively adjusts focus across variables such as CPI, M2, and interest rates based on shifts in the economic environment. This study proves that the DCL-MHA architecture provides a high-precision, high-stability decision-support solution for digital macro-control and economic risk early warning.