## 2 Materials and Methods
### Feature Selection
For any one category, the classifier accuracy \(P\) and recall \(R\) are defined as shown below. \[P=\frac{A}{A+C},\ R=\frac{A}{A+B} \tag{11}\]

A new parameter _F1-measure_ is defined to integrate the meaning of the two parameters \(P\) and \(R\), as shown below. \[F1-measure=\frac{2 RP}{R+P} \tag{12}\]

### Experimental Procedure

In this section, the effectiveness of the text classification algorithm under different feature vector scales is verified, and the performance gap between the traditional SISD-based method and the AVX-256 acceleration method is compared. 1. Construct the dataset. Five categories were selected from the total corpus of the experiment, and 2000 articles in each category were selected as the training set and 1000 articles as the test set. The composition of the dataset is shown in Table 3.
2. Text preprocessing and presentation. According to 10,000 training documents, the selected high-frequency words were used to construct a keyword dictionary of different sizes. 3. Feature selection. According to 10,000 training documents, the occurrence times of each keyword in each text were counted, and the weight of the feature vector was calculated according to the _tf-DE_ algorithm. 4. Training sample set. In this step, text representation and preprocessing were carried out on the training sample set, that is, the previous steps (2) and (3) were carried out on the training sample set to obtain the vector set of the training samples. 5. Classification test. First of all, text representation and preprocessing of the test set were required; this means that the previous steps (2) and (3) of the test set were executed to obtain the vector set of the test set. 6. Calculate the distance between the text to be tested and each sample in the training sample set, so as to find the nearest \(k\) neighbors, and then obtain the category type of the test text according to the types of \(k\) neighbors. 7. Obtain results of statistical classification. ### Results Analysis

The traditional SISD-based method and the AVX-256 acceleration method of the text classification algorithm are applied as contrasting experiments to observe and analyze the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Category** & **Finance** & **Education** & **Technology** & **Sports** & **Entertainment** & **Total** \\ \hline training set & 2000 & 2000 & 2000 & 2000 & 10,000 \\ test set & 1000 & 1000 & 1000 & 1000 & 1000 & 5000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Composition of dataset. performance of feature value selection, distance calculation, and nearest neighbor sorting of the test dataset. When the feature selection algorithm _tf-DE_ is selected for testing, the multi-classification effects of the kNN and SVM are shown in Table 4. The comparison results show that the _F1-measures_ of kNN and SVM models are comparable. The test results are shown in Figure 4, which shows that as the lexical diversity of the text in the entertainment category is low, and that high-frequency words in the entertainment category, such as film, director, and box office, can better represent the entertainment category. Therefore, when the feature vector dimension is small, a higher _F1-measure_ value can be achieved, and the classification accuracy is as high as 95.5% shown by the red line. However, in the text of the education category, the lexical diversity is high, and when the feature vector dimension is small, the accuracy rate is only 70.9% shown by the gray line. However, when the feature vector dimension is larger, the classification accuracy of the education category text can also achieve a higher _F1-measure_, of 85.9%.