## 1 Introduction  
Coral reef ecosystems are vital pillars of marine biodiversity and ecological balance, possessing immense ecological, economic, and social value. These ecosystems provide abundant marine resources and habitats, playing a crucial role in fisheries, tourism, and coastal protection. However, human activities, such as global climate change, marine pollution, and overfishing, are severely threatening the survival of coral reefs [1, 2]. Therefore, in-depth research and the protection of coral reef ecosystems have become key tasks in the field of marine science. In the realm of Natural Language Processing (NLP), Named Entity Recognition (NER) is a fundamental and crucial task. The goal of NER is to identify specific named entities in a text, such as names of people, places, organizations, dates, and quantities. In the marine domain, NER tasks are particularly challenging due to the lack of explicit word boundaries in Chinese texts, difficulties in tokenization, and the symbolic characteristics of Chinese characters [3], which add semantic weight. Although translating Chinese text into English can facilitate analysis, this approach may introduce errors and lose context-specific meanings [4], especially for domain-specific terms. Therefore, processing Chinese text directly ensures more precise and contextually accurate NER results. In the field of Natural Language Processing (NLP), Named Entity Recognition (NER) technology is an important tool for extracting key entity information from large volumes of text data. The evolution of NER technology has undergone multiple stages, from rule-based matching to deep learning. Initially, researchers relied on manually crafted rules for entity recognition, which proved inadequate when dealing with complex entity structures and diversity. In contrast, methods based on statistical models such as Hidden Markov Models (HMM) [5], Conditional Random Fields (CRF) [6], and Support Vector Machines (SVM) [7], despite relying on manual feature extraction and classifier training, have achieved some success in entity recognition tasks. However, these methods still face issues with inefficient feature extraction and over-reliance on domain expert knowledge. In the marine domain, [PERSON] et al. [8] proposed a method combining user dictionaries with CRF, enhancing recognition rates by deeply analyzing the features of marine-specific entities. Although this method improved recognition to some extent, it still faced issues related to the completeness of user dictionaries and limitations in feature selection. In the field of marine science, named entity recognition (NER) techniques rarely rely solely on statistical models, as their accuracy in entity extraction is typically low. With the advancement of deep learning technologies, an increasing number of researchers are integrating statistical models with deep learning methods. This approach not only enhances accuracy but also reduces dependence on manually defined features and external resources. In recent years, NER methods based on deep learning have made significant progress. Particularly, models such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) [9] have successfully addressed the issues of gradient vanishing and explosion in long sequence training, becoming the main models for this task. In 2016, [PERSON] et al. [10] introduced the BiLSTM-CRF model, pioneering the integration of statistical models with deep learning techniques for named entity recognition (NER). This approach, in comparison to using a standalone Conditional Random Field (CRF), incorporates contextual meanings related to individual words from both directions, thereby enhancing annotation accuracy. To improve the recognition of alien marine species entities, [PERSON] et al. [11] proposed the CNN-BiGRU-CRF model. This model incorporates an attention mechanism that mimics the human tendency to focus differently on various aspects.