In multimodal semantic segmentation tasks of urban street scenes, existing methods lack modeling of intermodal structural alignment and semantic cooperation between architectures, leading to insufficient fusion feature representations. To address this issue, this article proposes a novel structural optimization network: a hybrid embedding segment anything model (SAM) guided feedback network (GFNet). This network is based on the SAM framework and achieves multimodal structural alignment by transforming the semantic prior (SP) extractor through module-level fine-tuning of the image encoder. Furthermore, this article proposes a cross-architecture knowledge transfer (CAKT) mechanism, injecting the structural awareness capability of SAM into the backbone features of each layer, achieving dual optimization of alignment and enhancement. To address the issues of intermodal heterogeneity and semantic conflict, this article combines complementary fusion at different frequencies and cross-modal similarity enhancement strategies to achieve fine-grained semantic fusion and consistency modeling, supplemented by a dual-supervised constraint mechanism to improve modal independence and robustness. On several challenging datasets, mAcc is improved by about 5%, and GFNet demonstrates the superior segmentation performance and robustness compared to existing methods. Our code will be released to the public athttps://github.com/WBangG/GFNet

Abstract Improving health literacy through health information dissemination is one of the most economical and effective mechanisms for improving population health. This process needs to fully accommodate the thematic suitability of health information supply and demand and reduce the impact of information overload and supply–demand mismatch on the enthusiasm of health information acquisition. We propose a health information topic modeling analysis framework that integrates deep learning methods and clustering techniques to model the supply‐side and demand‐side topics of health information and to quantify the thematic alignment of supply and demand. To validate the effectiveness of the framework, we have conducted an empirical analysis on a dataset with 90,418 pieces of textual data from two prominent social networking platforms. The results show that the supply of health information in general has not yet met the demand, the demand for health information has not yet been met to a considerable extent, especially for disease‐related topics, and there is clear inconsistency between the supply and demand sides for the same health topics. Public health policy‐making departments and content producers can adjust their information selection and dissemination strategies according to the distribution of identified health topics, thereby improving the effectiveness of public health information dissemination.