Sintering, as a preliminary step in the blast furnace, has a profound influence on the ultimate quality of the iron product. Accurate forecasting of the chemical composition during sintering operations has become crucial to facilitate the production of higher quality inputs for downstream processes. However, modeling sintering is complex due to its long timescales, multistage transfers, and intricate redox reactions. To address this, a novel regression neural network based on orthogonal basis decomposition and reconstruction with implicit subspace identification is proposed. First, a recursive Fourier-transform-like enoding block is implemented to extract feature capturing long-term memory via orthogonal basis decomposition. Subsequently, an stochastic-gradient-based identification algorithm is used to approximate the ground truth system and model the output. The feasibility and utility of the approach are demonstrated using simulated and real-world sintering plant data. Considering encoding and identification separately offers deeper insights into sintering processes, resulting in enhanced explicability of model behaviors and a significant improvement of 22.15% loss reduction in forecasting performance.