## 4 Results and Discussion
### Research Results
#### 4.1.2 Fuzzy Analytical Hierarchical Process to Calculate the Weights of the Indicators  
Three executives of domestic AI technology enterprises and five full-time teachers from computer departments were invited to make a two-by-two comparison of the first-level and second-level indicators, which gave the consistency results shown in Tables 6 and 7. The weight calculation of the functionality in Table 6 is taken as an example; the matrix is a fourth-order matrix, n = 4, and the calculation for weight is as follows:  
\[\omega_{1}^{1} =\frac{M_{1}^{1}+\frac{n}{2}-1}{n(n-1)} =\frac{(0.50+0.65+0.80+0.69)+\frac{4}{2}-1}{4(4-3)}\] \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dimension**} & **Factor** & **Item** & **Sum of** & **Factor Load** & **Communality** & **Eigenvalue** & **Explanatory** & **Cumulative** \\  & **Naming** & **Number** & **Problems** & **Factor Load** & **Communality** & **Variance** & **Variance** & **Explanatory** \\ \hline \multirow{8}{*}{Security} & \multirow{4}{*}{Access security} & a412 & \multirow{4}{*}{4} & 0.878 & 0.772 & \multirow{4}{*}{3.186} & \multirow{4}{*}{36.564\%} \\ \cline{3-4} \cline{6-9}  & & a413 & & 0.848 & 0.740 & & & \\ \cline{1-1} \cline{3-4} \cline{6-9}  & & a411 & & 0.844 & 0.790 & & & \\ \cline{1-1} \cline{3-4} \cline{6-9}  & & a414 & & 0.795 & 0.633 & & & \\ \cline{1-1} \cline{3-4} \cline{6-9}  & & a417 & & 0.932 & 0.873 & & & \\ \cline{1-1} \cline{3-4} \cline{6-9}  & & a418 & & 0.904 & 0.857 & & & \\ \cline{1-1} \cline{3-4} \cline{6-9}  & & a416 & & 0.772 & 0.637 & & & \\ \cline{1-1} \cline{3-4} \cline{6-9}  & & a423 & & 0.708 & 0.507 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._Figure 3: Structure of factor analysis. The difference between any row of the data and the corresponding indicator data of other rows in this study is the identical constant, which indicates that there is a consistency test in this study. In the case of functionality, ease of use, system performance, and security, for example, the difference between the corresponding data of the indicators of the two groups is 0.15, which has equal values. The results of the factor analysis are shown in Figures 3 and 4. The weights of functionality, ease of use, system performance, and security are 0.3, 0.25, 0.2, and 0.24, respectively, which shows that functionality has a stronger influence. The weights of multi-layer image restoration, style transfer and migration, image editing, and image restoration are 0.19, 0.22, 0.29, and 0.3, respectively, which shows that image restoration is more powerful than the other three indicators. Media applicability, service quality, image editing, and image restoration are more important than the other three indicators. The weights of media applicability, service quality, interaction, visual harmony, and pattern recognition are 0.18, 0.2, 0.19, 0.26, and 0.17, respectively, which shows that the weights of the five indicators are not significant. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scale Valuer** & **Functionality** & **Ease of Use** &
\begin{tabular}{c} **System** \\ **Performance** \\ \end{tabular} & **Security** & \(\mathbf{\omega_{i}^{1}}\) \\ \hline Functionality & 0.50 & 0.65 & 0.80 & 0.69 & 0.30 \\ \hline Ease of use & 0.35 & 0.50 & 0.65 & 0.54 & 0.25 \\ \hline System performance & 0.20 & 0.35 & 0.50 & 0.39 & 0.20 \\ \hline Security & 0.31 & 0.46 & 0.61 & 0.50 & 0.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation matrix and weights of first-level evaluation indicators. Figure 4: Structure of factor analysis.