# IWF-TextRank Keyword Extraction Algorithm Modelling
## 3 Results
### Comparison of Results
#### 3.3.1 Comparison of Results for Different Features
In the double-feature experiment, the feature weights were obtained using the sequential relationship analysis method; the degree of importance between the indicators was lexicality > frequency > length, and the ratio of the degree of importance was lexicality/frequency/length 1:1.3, lexicality/length 1:1.5, and frequency/length 1:1.2, which were then calculated to obtain the weights of the features and inputted into the BP neural network training to obtain the feature parameters. In the three-feature experiment, word/word frequency/word length was 1.3:1.2, and the weights were calculated in the same way as in the two-feature experiment. The specific parameter settings are shown in Table 9, and the experimental results are shown in Table 10 and Figure 4, where the red curves represent the extraction results under the conditions of the feature parameters in this paper. Figure 3: Comparison of results for single features. (**a**) Comparison of _p_-values for single features; (**b**) comparison of R-values for single features; (**c**) comparison plot of F-values for single features. As can be seen from the figure, the results under the combination of features in this paper are significantly better than the results under the combination of double and triple features, and the extraction effect is better than the combination of two and two when combining word frequency, word length, and word nature; a comparative analysis of the \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Arithmetic**} & \multicolumn{3}{c}{**Accuracy (P)**} & \multicolumn{3}{c}{**Recall (R)**} & \multicolumn{3}{c}{**F-Value**} \\ \cline{2-13}  & **3** & **5** & **7** & **10** & **3** & **5** & **7** & **10** & **3** & **5** & **7** & **10** \\ \hline
5 & 0.725 & 0.696 & 0.647 & 0.608 & 0.220 & 0.350 & 0.448 & 0.577 & 0.337 & 0.466 & 0.529 & 0.592 \\
6 & 0.720 & 0.689 & 0.639 & 0.601 & 0.214 & 0.342 & 0.441 & 0.569 & 0.329 & 0.457 & 0.521 & 0.584 \\
7 & 0.723 & 0.692 & 0.642 & 0.605 & 0.216 & 0.346 & 0.443 & 0.573 & 0.333 & 0.461 & 0.525 & 0.589 \\
8 & 0.728 & 0.699 & 0.649 & 0.610 & 0.225 & 0.353 & 0.451 & 0.580 & 0.344 & 0.469 & 0.534 & 0.595 \\
9 & 0.732 & 0.704 & 0.652 & 0.613 & 0.229 & 0.357 & 0.456 & 0.584 & 0.348 & 0.473 & 0.537 & 0.598 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Multi-feature results. Figure 4: Comparison of multiple features. (**a**) Comparison of \(p\)-values for multiple features; (**b**) comparison of R-values for multiple features; (**c**) comparison F-values for multiple features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Serial**} & \multirow{2}{*}{**Diagnostic Property**} & \multirow{2}{*}{\(\alpha\)} & \multirow{2}{*}{\(\beta\)} & \multirow{2}{*}{\(\gamma\)} & \multirow{2}{*}{\(\delta\)} \\ \cline{1-1}
**Number** & & & & & & \\ \hline
5 & Word Frequency—Word Properties & 0.41 & 0.59 & 0.00 & 0.00 \\
6 & Word Frequency—Word Length & 0.56 & 0.00 & 0.44 & 0.00 \\
7 & Lexical Category—Word Length & 0.00 & 0.62 & 0.38 & 0.00 \\
8 & Word Frequency—Word Length & 0.32 & 0.41 & 0.26 & 0.00 \\  & Word & & & & & \\
9 & Frequency—Lexicality—Word Length—Semantics & 0.33 & 0.35 & 0.21 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Multi-feature parameter settings. results between the combination of double features reveals that the extraction effect is the best when combining word frequency and word nature. The above experimental comparison found that the \(p\)-value, R-value, and F-value, under the conditions of the features selected in this paper, were greater than the other feature combinations, indicating that the features selected in this paper are more suitable for the text dataset.