## 4 Results and Discussion
### Weight Analysis of the Evaluation Indexes Using the ANP Method
#### 4.3.1 Single-Factor Fuzzy Evaluation and Construction of the Fuzzy Relation Matrix  
The weight set composition of the indexes at all levels was as follows:  
The first-level index weight set of a maturity evaluation was \(W_{Z}=\{0.3568\), 0.0805, 0.3617, 0.2010\(\}\). The second-level index weight sets of the maturity evaluation were as follows:  
\(W_{IS}=[0.3568]\), \(W_{IT}=\{0.0805\}\), \(W_{IB}=[0.0593,0.2348,0.0676]\), \(W_{IO}=[0.1361,0.0649]\)  
The single-factor fuzzy comprehensive evaluation reflects the influence of a single factor on the maturity target. This study conducted a single factor evaluation on \(n_{i}\) elements of the third-level index set \(U_{Ai}=\{u_{1}{}^{(0)},u_{2}{}^{(0)},\ldots,u_{n}{}^{(0)}\}\) of enterprise AI technology application maturity from the bottom up, and a fuzzy map was obtained: \(f_{Bi}\): \(U_{Bi}\to F(V)\). According to fuzzy map \(f\), the fuzzy relation \(R_{f}\) can be derived, and the evaluation fuzzy set of all third-level indexes belonging to the two indexes can be calculated. The method used to determine the fuzzy matrix was to have industry experts to form an \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **First-Level** \\ **Indexes** \\ \end{tabular} & **Weight** & \begin{tabular}{c} **Second-** \\ **Level** \\ **Indexes** \\ \end{tabular} & **Weight** & \begin{tabular}{c} **Third-Level** \\ **Indexes** \\ \end{tabular} & **Local Weight** & \
\begin{tabular}{c} **Global** \\ **Weight** \\ \end{tabular} & **Rank** \\ \hline IS & 0.3568 & ISA & 0.3568 & ISA2 & 0.3193 & 0.1139 & 2 \\  & & & ISA3 & 0.5672 & 0.2024 & 1 \\ \hline IT & 0.0805 & ITA & 0.0805 & ITA1 & 0.3986 & 0.0321 & 12 \\  & & & ITA2 & 0.3549 & 0.0286 & 14 \\  & & & ITA3 & 0.2465 & 0.0198 & 18 \\ \hline IB & & & IBA & 0.0593 & IBA1 & 0.4278 & 0.0254 & 15 \\  & & & IBA2 & 0.5722 & 0.0339 & 11 \\ \cline{3-6}  & & & IBB1 & 0.2984 & 0.0701 & 3 \\  & & & IBB2 & 0.2122 & 0.0498 & 7 \\  & & & IBB3 & 0.1899 & 0.0446 & 8 \\  & & & IBB4 & 0.2239 & 0.0526 & 5 \\  & & & IBB5 & 0.0756 & 0.0178 & 20 \\ \cline{3-6}  & & & IBC & 0.0676 &
\begin{tabular}{c} IBC1 \\ IBC2 \\ \end{tabular} & 0.7696 & 0.0520 & 6 \\  & & & IBC2 & 0.2304 & 0.0156 & 21 \\ \hline IO & & & & IOA1 & 0.2146 & 0.0292 & 13 \\  & & & IOA2 & 0.3260 & 0.0444 & 9 \\  & & & IOA3 & 0.1604 & 0.0218 & 17 \\  & & & IOA4 & 0.1333 & 0.0181 & 19 \\  & & & IOA5 & 0.1657 & 0.0226 & 16 \\ \cline{3-6}  & & & IOB & 0.0649 & IOB1 & 1.0000 & 0.0649 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Weights and ranks of the indexes. evaluation group and evaluate the grade through scoring, and a fuzzy relationship matrix of single-factor evaluations was obtained. For example, when evaluating the third-level indexes \"intelligence strategic thinking\" of Enterprise Q, 70% of the experts thought it was at the \"leading level\", 10% of the experts thought it was at the \"optimization level\", 10% of the experts thought it was at the \"integration level\", 5% of the experts thought it was at the \"specification level\", and 5% of the experts thought it was at the \"planning level\". Then, the membership degree of the intelligence strategic thinking index for all levels of evaluation were obtained: ISA1\(\rightarrow\)(0.7, 0.1, 0.1, 0.05, 0.05). The fuzzy relationship matrix of the single-factor evaluation can be obtained by analogy. \[R_{xy}= \begin{bmatrix}r_{11}&r_{12}&r_{13}&r_{14}&r_{15}\\ r_{21}&r_{22}&r_{23}&r_{24}&r_{25}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ r_{n1}&r_{n2}&r_{n3}&r_{n4}&r_{n5}\end{bmatrix} .\]  
In this study, \(n\) is the number of the third-level indexes included in each secondary index, where \(n=1\), \(2\), \(3\), \(5\), \(x=1\), \(2\), \(3\), \(4\), and \(y=1\), \(2\), \(3\). \(r_{\bar{y}}\) represents the membership degree of factor \(i\) to the evaluation level \(j\), reflecting the fuzzy relationship between factor set \(U\) and comment set \(V\), according to which, multiple single-factor evaluation matrices can be obtained:  
\[R_{11}=