## 3 Experiments and Results
### Algorithm Parameter Selection  
(1) Feature vector training.  
The datasets after word segmentation and word segmentation are used as word vector and word vector training corpus, respectively. The Word2 Vec tool in the Gensim package of Python language is used for training, and the word vector and word vector models are constructed. The Word2 Vec parameter settings are shown in Table 4.  
(2) Algorithm parameter setting.  
The number of LSTM units in the BiLSTM network needs to be determined. The number of neurons determines the feature extraction capability of the layer, increasing the number of neurons that can retain more feature information. However, an excessive number of neurons increases the computational time of the algorithm and has limited effects on improving the results. Therefore, this paper sets the number of LSTM units to 20 so as to balance the time cost while retaining as much feature information as possible.  
The main parameters of the CNN include: (1) kernel size, which is the size of the area for convolving the input feature vector; (2) the number of convolutional kernels; (3) dropout ratio, which is the proportion of trained parameters that are randomly dropped out; (4) batch_size of the stochastic gradient descent algorithm; (5) number of iterations; (6) optimization function; (7) Self-Attention output dimension.  
The Sac-BiLSTM uses kernel sizes of three, five and seven. The more convolutional kernels used, the more features can be extracted by the layer. To enhance computational efficiency, the number of convolutional kernels is usually set to \(2^{n}\), where \(n\) is a positive integer, so the number of convolutional kernels is set to 128. Generally, the larger the batch_size, the more accurate the determined descent direction, and the smaller the training oscillations. However, more epochs are required to achieve the same accuracy. Thus, to achieve the optimal balance between time and convergence accuracy, the batch_size is set to 64. In addition, the dimensionality of Self-Attention is set to maintain the same dimensionality in order to redistribute weights for previous outputs without losing useful information.  
To explore deeper data information, the proposed Sac-BiLSTM algorithm adopts a stacked form of multiple convolutional layers. The length of the text ranges from a minimum of 10 characters to a maximum of 1900 characters. Therefore, the use of three convolutional layers can more accurately extract features. The sizes of the convolutional filters are set to three, five, and seven. The more convolutional filters, the more features the layer extracts, and the number of convolutional filters is generally set as \(2^{n}\), where \(n\) is a positive integer. Hence, the number of convolutional filters is set to 128.  
In general, a larger batch size leads to a more accurate descent direction, resulting in smaller training oscillations. However, it requires more epochs to achieve the same accuracy. To achieve the best balance between time and convergence accuracy, the batch size is set to 64. Additionally, the dimension of the Self-Attention layer is set to be the same as the previous layer to redistribute weights and avoid losing valuable information.  
After 15 training iterations, the accuracy on the validation set reaches its maximum value and stabilizes in subsequent training. Therefore, the number of iterations is set to 15. Compared to traditional gradient descent algorithms, the Adam algorithm has high computational efficiency and requires less memory. It also prevents excessive learning step  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Character** & **Word** \\ \hline sg & Skip-gram & Skip-gram \\ size & 300 & 300 \\ min\_count & 3 & 3 \\ window & 10 & 10 \\ workers & 4 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Word2 Vec parameter Settings.  
size through bias correction terms. Thus, the final parameter values are determined as shown in Table 5.