The performance indicators of sintered ore fully reflect its quality, and the quality of sintered ore, in turn, enhances blast furnace production efficiency, reduces energy consumption and fuel ratios, and promotes green smelting and environmental protection. In the process of predicting sintered ore quality, traditional deep neural networks suffer from poor interpretability, while fuzzy neural networks, which offer strong interpretability, are prone to issues such as rule explosion and difficulties in parameter tuning. This paper constructs a predictive model that combines fuzzy neural networks with deep neural networks. First, by improving the CBAM channel attention module, the model calculates both channel and spatial attention for input features to fuse effective features; this enhances the model′s ability to effectively model complex nonlinear relationships and dynamically allocate feature importance. Furthermore, by optimizing the model using an improved Grey Wolf optimization algorithm, the model′s predictive accuracy is improved. Finally, experimental studies were conducted on the prediction of sintered ore drum index, sintered ore alkalinity, and RDI+3.15, achieving high accuracy and validating the feasibility of the proposed model and algorithm. A comparison of the four models—GW-FNN, GW-DFNN, Attention-DFNN, and GW-Attention-DFNN—revealed that the GW-Attention-DFNN model achieved an R2 of 0.968 2 for the drum index, 0.975 0 for sintered ore alkalinity (R), and 0.964 2 for RDI+3.15. These results indicate that this model performs well in predicting the quality performance of sintered ore.