## 1 Introduction
By introducing the attention mechanism, the model selectively emphasizes critical information while de-emphasizing other concurrent inputs. In text processing, this translates to assigning higher weights to key texts and reducing the weights of less relevant texts. The model initially uses BiGRU to learn and represent the contextual information of alien species at the text level, and then employs the attention mechanism to capture the salient semantic features of marine species entities. This approach mitigates the issue of long-distance dependencies in textual data, thereby enhancing the accuracy of marine species entity recognition and providing a reference for the identification of marine organisms and concepts in the future. Recognizing the dependency of the attention mechanism on external factors, [PERSON] et al. [12] further incorporated the multi-head self-attention mechanism into the recognition of entities in Chinese marine text data. They used knowledge graph embedding vectors and BiLSTM output vectors jointly as input vectors for the self-attention mechanism. This approach considers both the internal correlation of features and long-sequence dependencies, thereby improving the accuracy of alien marine species entity recognition and enhancing the overall entity recognition capability of the corpus. The successful application of these techniques to marine data underscores the promising future of multi-head attention mechanisms in the marine domain. [PERSON] et al. [13] introduced a model that integrates attention mechanisms, an expanded convolutional neural network (IDCNN), and CRF, aimed at enhancing the ability to recognize marine natural product (MNP) entities from unstructured texts. In the biomedical field, a study by [PERSON] et al. [14] demonstrated the application of NER and relationship detection in biomedical information extraction. They adopted a comprehensive approach that effectively extracted entity and relationship information from biomedical literature, indicating that deep learning models have broad applicability in handling specialized domain texts. Moreover, pre-trained language models such as BERT, GPT, and RoBERTa [15; 16; 17; 18] have significantly improved the capture of contextual information and semantic representation through large-scale text data pre-training, further enhancing NER performance. Despite these advances, applying deep learning models to named entity recognition in the Chinese marine domain still poses numerous challenges. The uniqueness of Chinese text, the complexity of marine-specific terminologies, and the abstract nature of relationships between entities make traditional character-based models inadequate. Moreover, most existing pre-trained models are trained on general corpora and are not specifically optimized for the marine domain, resulting in less than satisfactory performance when dealing with specialized terminology. Therefore, the task of entity recognition in the marine domain requires not only identifying the entities themselves but also understanding the complex relationships between them, which demands higher comprehension capabilities from the models. In light of these challenges, this study introduces a novel model architecture designed to enhance the performance of Chinese marine named entity recognition by integrating deep learning models with greater domain adaptability, Chinese linguistic features, and marine-specific knowledge. Specifically addressing the recognition of long and nested entities in coral reef ecosystem texts, our model architecture is capable of effectively identifying these complex entities while adapting to the characteristics of Chinese text and the complexity of marine science. Experimental validation on publicly available marine science coral reef ecosystem text datasets demonstrates the effectiveness and superiority of our proposed model in the task of Chinese marine named entity recognition.