Boilers are significant contributors to carbon emissions and pollutants across various industrial sectors. Accurately modeling furnace temperature is critical for optimizing combustion and enhancing operational efficiency. However, modeling poses significant challenges due to the interplay between rapidly changing dynamic processes and slowly varying static data. To address the coupling and redundancy inherent in these heterogeneous features, a hybrid framework for boiler temperature modeling (HFBTM) is proposed in this paper. The framework utilizes a multi-layer dense network to extract static features and a selective state space model (S3M) to capture dynamic features. These features are effectively combined through a hybrid feature fusion module using weighted integration, generating accurate temperature predictions across multiple future time steps. Compared with traditional single dynamic models, HFBTM mitigates information redundancy, reduces error propagation, and serves as an end-to-end furnace temperature prediction model that integrates both static and dynamic features. Experimental results demonstrate that the HFBTM framework delivers superior prediction performance across forecasting tasks of varying lengths. Compared with the existing methods, the proposed framework achieves higher accuracy and meets the requirements for precise modeling of boiler systems.