## 5 Conclusions  
This study addresses the challenges of non-standardization and limited annotation resources in Chinese marine domain texts, particularly with complex entities like long and nested entities in coral reef ecosystem-related texts. Traditional NER methods often fail to capture deep semantic features, leading to inefficiencies and inaccuracies. To overcome these challenges, we present a Named Entity Recognition (NER) model, termed the BERT+BiGRU+Att+CRF model, specifically designed for marine coral reef ecosystems. The BERT+BiGRU+Att+CRF model leverages BERT to acquire rich contextual information and utilizes BiGRUs to effectively process sequence dependencies, thereby capturing sentence-level global semantics more accurately. The introduction of an attention mechanism enhances the model's focus on key features, which is particularly important for extracting complex semantic features related to coral reefs. In the final stage, the CRF layer outputs the optimal sequence of entity labels by considering the transition probabilities between tags. Through a series of experiments on a specialized coral reef ecosystem corpus, the BERT+BiGRU+Att+CRF model achieved an F1 Score of 86.54%, significantly outperforming other mainstream models. This demonstrates the model's superiority in the task of entity recognition within marine coral reef ecosystems. Our research contributes to the field in three key ways: (1) Efficient NER framework: We designed an efficient NER framework for marine domain texts, which significantly improves the recognition of long and nested entities. (2) Enhanced model with attention mechanism: By integrating the attention mechanism, we enhanced the model's ability to recognize complex entity structures, addressing the limitations of existing methods in capturing deep semantic features. (3) Foundation for marine knowledge graphs: This work provides new tools and perspectives for the construction of knowledge graphs in the marine domain, laying a solid foundation for future research and advancing the development of marine domain text analysis technology. In future work, we aim to explore more advanced pre-trained models to further enhance the performance of the BERT+BiGRU+Att+CRF model and reduce training time. We also plan to expand the training dataset to boost the model's robustness and generalization ability. Additionally, we will consider applying the model to a broader range of NER tasks to verify its applicability across different domains, thereby contributing valuable references for related research fields. To advance textual analysis in both English and Chinese, it is essential to develop language-specific tools that consider the unique linguistic features of each language. Future research should focus on creating more sophisticated algorithms and models that can accurately process and analyze texts in different languages.  
**Author Contributions:** Conceptualization, D.Z. and X.C.; methodology, D.Z. and X.C.; software, X.C.; validation, D.Z., Y.C. and X.C.; resources, Y.C. and X.C.; data curation, X.C.; writing--original draft preparation, X.C.; writing--review and editing, D.Z. and Y.C.; funding acquisition, D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, the Youth Science Foundation Project (Grant Nos. 42106190, 62102243), and Shanghai Science and Technology Commission part of the local university capacity building projects (Grant No. 20050501900).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. For the experimental code of this paper, you can contact the author via e-mail. E-mail: [EMAIL_ADDRESS].  
We would like to thank the anonymous reviewers for their insightful comments and substantial help in improving this paper.  
The authors declare no conflicts of interest.