## 1 Introduction
### Encoder Layer: BiGRU Layer  
The Gated Recurrent Unit (GRU) is an improved recurrent neural network (RNN) unit structure [21], demonstrating higher efficiency and better performance than traditional RNNs and LSTMs when dealing with long-term dependencies [22; 23]. By introducing  
Figure 3: BERT network model. Figure 2: Example diagram of BERT input. two gating mechanisms--the reset gate and the update gate--GRU effectively addresses the vanishing gradient problem and optimizes the control of information flow. The reset gate determines how much of the past information to retain at the current time step, while the update gate decides how the new information is integrated into the current state. This structural design not only enhances the model's understanding of sequential data but also boosts its generalization capability in small-sample learning by reducing the number of parameters. These attributes make GRU particularly valuable in scenarios with limited resources or where rapid training is required. In practical applications, whether in natural language processing or time series prediction [24,25], GRU has demonstrated its robust modeling capabilities. The internal structure of GRU is illustrated in Figure 4.  
The GRU combines the current node input \(x^{t}\) and the state \(h^{t-1}\) transmitted from the previous node to produce the current node's output \(y^{t}\) and the hidden state \(h^{t}\) passed to the next node. The internal parameter transmission and update formulas in the network are shown in Equations (1)-(4). \[r^{t} =\sigma\Big{(}w^{rx}x^{t}+w^{rh}h^{t-1}+b^{r}\Big{)} \tag{1}\] \[z^{t} =\sigma\Big{(}w^{zx}x^{t}+w^{zh}h^{t-1}+b^{z}\Big{)}\] (2) \[h^{t} =\tanh\Big{(}w^{rh}x^{t}+r^{t}\odot w^{hh}h^{t-1}\Big{)}\] (3) \[h^{t} =\big{(}1-z^{t}\big{)}\odot h^{t-1}+h^{t}\odot z^{t} \tag{4}\]  
In these formulas, \(\sigma\) denotes the _sigmoid_ function, which serves as a gate control signal to keep values within the [0,1] range. The closer the gate signal is to 1, the more data are remembered. Conversely, more are forgotten. \(r\) is the reset gate control, and \(z\) is the update gate control. \(h^{t}\) indicates the candidate's hidden state. \(w^{rx}\), \(w^{rh}\), and others are weight matrices, while \(b^{r}\), \(b^{z}\), etc., are bias terms. \(\odot\) represents the Hadamard product, meaning the element-wise multiplication of matrices. The reset gate computes the data after reset \(r^{t}\odot h^{t-1}\), which is then concatenated with \(x^{t}\) and passed through a \(tanh\) activation function to keep the output value within [\(-\)1,1], resulting in the hidden state \(h^{t}\). Simultaneously, the update gate performs forgetting and selective memory functions, where \(\big{(}1-z^{t}\big{)}\odot h^{t-1}\) selectively forgets the state of previous nodes, and \(h^{t}\big{\cap}\odot z^{t}\) selectively remembers the hidden state. In named entity recognition tasks, contextual information is paramount for predicting entity labels. To acquire contextual information from both the past and future within the text, bidirectional gated recurrent unit networks (BiGRUs) [26] are commonly utilized. The hidden state \(h^{t}\) of the GRU can only access information from past contexts, not future ones. In processing text data for Chinese marine coral reef ecosystem studies, named entity recognition plays a vital role. It aims to identify crucial ecological entities within complex natural language texts, like species names, places, ecological phenomena, etc. Given the  
Figure 4: Internal structure of GRU. unique features of Chinese texts, such as flexible word order and the absence of clear word boundary delimiters, this task poses significant challenges. Traditional unidirectional GRUs might not adequately capture the contextual information around an entity, particularly subsequent information, potentially impacting the precision and completeness of entity recognition.