## 3 Experiment and Results Analysis
### Experimental Results and Analysis  
This study introduces a deep learning model that integrates BERT, BiGRU, and CRF, enhanced by an attention mechanism, to improve the recognition of complex entity structures. The model utilizes BERT to capture context-relevant character vectors, employs BiGRU to extract global semantic features, incorporates an attention mechanism to focus on key information, and uses CRF to produce optimized label sequences. We constructed a specialized coral reef ecosystem corpus to evaluate the model's performance through a series of experiments. To validate the effectiveness of the proposed BERT+BiGRU+Att+CRF model, two sets of comparative experiments were conducted, as detailed in Tables 3 and 4. The first set included the proposed BERT+BiGRU+Att+CRF model, along with BERT+BiLSTM+Att+CRF, BERT+BiGRU+CRF, BERT+BiLSTM+CRF, BiGRU+CRF, BiLSTM+CRF and CRF models for comparison. The results, as seen in Table 3 and Figure 8, compare the impact of different algorithmic models on named entity recognition. It was observed that BiGRU, in comparison to BiLSTM, shows slightly superior performance without the integration of other technologies (Model 2 vs. Model 3). Introducing the BERT pre-trained model (Models 4 and 5 vs. Models 2 and 3) significantly enhances all performance metrics, highlighting BERT's effectiveness in improving entity recognition. Further, the incorporation of the attention mechanism (Attention) in Models 6 and 7, as compared to Models 4 and 5, markedly improves performance, with Model 7 demonstrating enhancements in all metrics. This analysis suggests that BiGRU, relative to BiLSTM, may provide a more favorable performance-time cost ratio, especially when combined with the BERT pre-trained model and attention mechanism. The BERT pre-trained model, by leveraging semantic information across different contexts to generate dynamic word embeddings, effectively addresses polysemy. The attention mechanism further boosts the model's ability to distill semantic features, thereby enhancing entity recognition performance. Notably, Model 7 (BERT-BiGRU+Att+CRF) not only delivers robust performance but also reduces training time by employing BiGRU instead of BiLSTM, offering savings in time costs. The second set of comparative experiments involved the proposed model and the current mainstream named entity recognition models, namely, the IDCNN (Iterative Dilated Convolution)+CRF model and the BERT+IDCNN+CRF model. The overall recognition performance of each model in the second set of experiments is shown in Table 4 and Figure 9. These results indicate that the performance of the IDCNN+CRF combination, when augmented with the BERT pre-trained model, significantly surpasses that of the standalone IDCNN+CRF model. However, the proposed BERT+BiGRU+Att+CRF model further improves across all three metrics, demonstrating the highest Precision, Recall, and F1 Scores. This indicates that the model proposed in this paper, BERT+BiGRU+Att+CRF, outperforms mainstream named entity recognition models in terms of entity recognition effectiveness. Analyzing the results of these two sets of experiments, it is evident that the BERT+BiGRU+Att+CRF model is effective in the task of named entity recognition within marine coral reef ecosystem contexts, especially when compared against the mainstream IDCNN+CRF model. This superiority in recognition accuracy can be attributed to the richer context word vectors provided by the BERT model, the more effective sequence processing capability of the BiGRU units, and the attention mechanism's capacity to weigh important information. Additionally, the CRF layer ensures that the predicted label sequence is globally optimal, enhancing the accuracy of boundary recognition. The synergistic effect among