In the era of big-data-driven multi-platform and multimodal health information dissemination, the rapid spread of false and misleading content poses a critical threat to public health awareness and decision making. To address this issue, a dual-stream Transformer-based multimodal health misinformation detection framework is presented, incorporating a symbol drift detection module, a symbol-aware text graph neural network, and a crossmodal alignment fusion module. The framework enables precise identification of implicit misleading health-related symbols, comprehensive modeling of textual dependency structures, and robust detection of crossmodal semantic conflicts. A domain-specific health-symbol-sensitive lexicon is constructed, and contextual drift intensity is quantitatively measured and embedded as explicit features into the text GNN. Bidirectional cross-attention and contrastive learning are further employed to enhance crossmodal semantic alignment. Extensive experiments on a large-scale real-world multimodal health information dataset, encompassing heterogeneous data sources typical of big data environments, demonstrate that the proposed method consistently outperforms state-of-the-art baselines in CTR prediction, multimodal recommendation, and ranking tasks. The results indicate substantial improvements in both accuracy and ranking quality, while ablation studies further verify the contributions of symbol drift modeling, graph-structured representation, and crossmodal fusion. Overall, the proposed approach advances big data analytics for multimodal misinformation detection and provides an interpretable and scalable solution for public health communication governance.

Under the growing demand for processing multimodal and cross-lingual information, traditional retrieval systems have encountered substantial limitations when handling heterogeneous inputs such as images, textual layouts, and multilingual language expressions. To address these challenges, a unified retrieval framework has been proposed, which integrates visual features from images, layout-aware optical character recognition (OCR) text, and bilingual semantic representations in Chinese and English. This framework aims to construct a shared semantic embedding space that mitigates semantic discrepancies across modalities and resolves inconsistencies in cross-lingual mappings. The architecture incorporates three main components: a visual encoder, a structure-aware OCR module, and a multilingual Transformer. Furthermore, a joint contrastive learning loss has been introduced to enhance alignment across both modalities and languages. The proposed method has been evaluated on three core tasks: a single-modality retrieval task from image → OCR, a cross-lingual retrieval task between Chinese and English, and a joint multimodal retrieval task involving image, OCR, and language inputs. Experimental results demonstrate that, in the joint multimodal setting, the proposed model achieved a Precision@10 of 0.693, Recall@10 of 0.684, nDCG@10 of 0.672, and F1@10 of 0.685, substantially outperforming established baselines such as CLIP, LayoutLMv3, and UNITER. Ablation studies revealed that removing either the structure-aware OCR module or the cross-lingual alignment mechanism resulted in a decrease in mean reciprocal rank (MRR) to 0.561, thereby confirming the critical role of these components in reinforcing semantic consistency across modalities. This study highlights the powerful potential of large language models in multimodal semantic fusion and retrieval tasks, providing robust solutions for large-scale semantic understanding and application scenarios in multilingual and multimodal contexts.

With the widespread application of high-resolution remote sensing imagery and unmanned aerial vehicle technologies in agricultural scenarios, accurately characterizing spatial pest diffusion from multi-temporal images has become a critical issue in intelligent agricultural management. To overcome the limitations of existing machine learning approaches that focus mainly on static recognition and lack effective spatio-temporal diffusion modeling, a UAV-based pest diffusion prediction and simulation framework is proposed. Multi-temporal UAV RGB and multispectral imagery are jointly modeled using a graph-based representation of farmland parcels, while temporal modeling and environmental embedding mechanisms are incorporated to enable simultaneous prediction of diffusion intensity and propagation paths. Experiments conducted on two real agricultural regions, Bayan Nur and Tangshan, demonstrate that the proposed method consistently outperforms representative spatio-temporal baselines. Compared with ST-GCN, the proposed framework achieves approximately 17–22% reductions in MAE and MSE, together with 8–12% improvements in PMR, while maintaining robust classification performance with precision, recall, and F1-score exceeding 0.82. These results indicate that the proposed approach can provide reliable support for agricultural information systems and diffusion-aware decision generation.

In this study, a cross-hierarchical intelligent modeling framework integrating an ecological semantic encoder, a distribution-aligned contrastive loss, and a disturbance-aware attention mechanism was developed to address the semantic alignment challenge between aboveground vegetation and belowground seed banks within forest ecosystems. The proposed framework leverages artificial intelligence and deep learning to characterize the structural and functional coupling between vegetation and soil communities, thereby elucidating the ecological mechanisms that underlie forest regeneration and stability. Experiments using representative forest ecological plot datasets demonstrated that the model achieved a top-1 accuracy of 78.6%, a top-5 accuracy of 89.3%, a mean cosine similarity of 0.784, and a reduced Kullback–Leibler divergence of 0.128, while the Jaccard index increased to 0.512—surpassing traditional statistical and machine-learning baselines such as RDA, CCA, Procrustes, Siamese, and SimCLR. The model also reduced NMDS stress to 0.094 and improved the Sørensen coefficient to 0.713, reflecting high robustness and precision in reconstructing community structure and ecological distributions. Additionally, the integration of distribution alignment and disturbance-aware mechanisms allows the model to capture dynamic vegetation–soil feedbacks across environmental gradients and disturbance regimes. This enables more accurate identification of regeneration potential, resilience thresholds, and restoration trajectories in degraded forests. Overall, the framework provides a novel theoretical foundation and a data-driven pathway for applying artificial intelligence to forest ecosystem monitoring, degradation diagnosis, and adaptive management for sustainable recovery.