Accurate prediction of flotation concentrate grade is essential for maintaining product quality and supporting timely process adjustment. However, mechanistic prediction remains difficult because industrial flotation involves nonlinear multivariable interactions and partially observed operating states. Recent advances in machine learning have increased interest in data-driven methods, with encouraging results. Although recent studies have incorporated temporal dependencies into flotation-grade prediction, explicitly modeling the condition-dependent evolution of multivariate process–quality relationships under changing plant conditions remains challenging. In this study, industrial flotation records were systematically analyzed from a data-driven perspective to identify challenges affecting concentrate-grade prediction. Statistical analyses showed that variable–grade relationships are condition-dependent and vary across operating periods, indicating that measured variables only partially characterize evolving flotation states and that fixed input–output mappings may be inadequate. Accordingly, a DGraFormer-based model was developed to capture evolving inter-variable dependencies and multi-scale temporal patterns. For one-hour-ahead forecasting, the model achieved an RMSE of 0.7587 and an $R^2$ of 0.5423 for iron concentrate grade, and an RMSE of 0.6917 and an $R^2$ of 0.6384 for silica concentrate grade, outperforming the evaluated machine-learning and deep-learning baselines overall. These results underscore the importance of dynamic multivariate modeling for accurate concentrate-grade forecasting.