Based on the BERTopic model, this study analyzes 15,744 scientific papers in the field of functional agriculture from 1995 to 2024 to uncover core themes and evolutionary trends in global functional agriculture, and particularly focuses on revealing the developmental trajectory in China. The results indicate that global functional agriculture research is characterized by diverse themes and intensive study, forming a multi-topic cross-network centered on plant chemical extraction and agricultural soil research, with a focus on food nutrition, human health, and environmental protection. By contrast, China’s functional agriculture research demonstrates a more focused and in-depth approach, concentrating on functional food development and agricultural environmental protection themes, with notable growth trends in areas such as selenium-enriched products and resistant starch. Combined with China’s agricultural development environment, this study makes the following suggestions for the development of functional agriculture in China: (1) Promoting interdisciplinary cooperation between functional agriculture and other technologies. (2) Developing agricultural products with Chinese characteristics and forming Chinese functional agricultural product brands. (3) Utilizing smart farming technology to boost functional agriculture.

Identifying frontier interdisciplinary domains is essential for tracking scientific evolution and informing strategic research planning. This study proposes a comprehensive framework that integrates (1) semantic disciplinary classification using a large language model (GPT-3.5-Turbo), (2) quantitative metrics for interdisciplinarity (degree and integration strength) and frontierness (novelty, growth, and impact), and (3) trend prediction using time series models, including Transformer, LSTM, GRU, Random Forest, and Linear Regression. The framework systematically captures both structural and temporal dimensions of emerging research fields. Compared to conventional citation-based or topic modeling approaches, it enhances semantic precision, supports multi-label classification, and enables forward-looking forecasts. Empirical validation shows that the Transformer model achieved the highest predictive performance, outperforming other deep learning and baseline models. As an illustrative example, the framework was applied to synthetic biology, which demonstrated high interdisciplinarity, strong novelty, and growing academic influence. These results underscore the field’s strategic position as a frontier interdisciplinary domain. Beyond this case, the proposed framework is generalizable to other domains and provides a scalable, data-driven solution for dynamic monitoring of emerging interdisciplinary areas. It holds promise for applications in science and technology intelligence, research evaluation, and policy support.

A large number of potential knowledge associations in scientific and technological innovation activities have not yet become explicit. How to identify potential valuable innovation opportunity clues from complex knowledge structures has therefore become an important issue in intelligence analysis research. This study takes the field of rice drought-tolerant breeding as an empirical case. Based on PMC full-text literature data, the LightRAG model was employed to extract innovation-related entities and their semantic relationships, including varieties, genes, proteins, phenotypes, and technological methods. A technology-data heterogeneous network for rice drought-tolerant breeding was then constructed, and the HetGNN link prediction method was introduced to predict potential relationships within the network. To evaluate the effectiveness of the proposed model, literature published from 2006 to 2020 was used to construct the training network, while newly emerging relationships extracted from literature published between 2021 and 2023 were used as a future validation set. Adamic-Adar and Node2Vec were further selected as baseline models for comparison. The experimental results show that the proposed method achieved an AUC of 0.8901, an AP of 0.9190, and an F1@0.5 of 0.8322 on the internal testing set, outperforming the baseline models in overall performance. In the temporal holdout validation, the model was able to identify some newly emerging knowledge associations that subsequently appeared in the 2021–2023 literature. The prediction results based on the full dataset indicate that the potential relationships are mainly concentrated in influence relationships between data elements and drought-tolerant phenotypes, as well as support relationships between technological methods and drought-tolerant phenotype research. This study constructs an analytical framework consisting of “innovation element extraction, heterogeneous network modeling, temporal holdout validation, and potential relationship interpretation,” thereby providing a methodological reference for identifying potential innovation opportunity clues from complex scientific knowledge structures.