## 3 Establishment of Core Indicator System
### Core Indicator System  
As the indicators identified above are still on the high side, it may lead to redundant information and an increase in the complexity of the analysis. Therefore, this paper uses exploratory factor analysis to recombine the original core indicators into a new set of mutually unrelated composite indicators to replace the original core indicators. The specific implementation is shown below:  
1. Standardizing the data. In this paper, the data are standardized using the Z-Score method, which uses the standard deviation as a ruler to measure the distance that a particular raw score deviates from the mean, which contains a few standard deviations and Z-Scores. Thus, the position of these data in the whole data is determined. \(Z\) is determined as \[Z=\frac{X-\overline{X}}{S}\] (5) where \(X\) is the raw data, \(\bar{X}\) is the mean, and \(S\) is the standard deviation. 2. The KMO test and Bartlett's test are performed, and the standardized data are brought through the SPSSPRO platform to determine whether factor analysis could be performed. The results of the tests are represented in Table 4. \begin{table}
\begin{tabular}{c c c} \hline
**Level 1 Indicators** & **Level 2 Indicators** & **Level 3 Indicators** \\ \hline \multirow{8}{*}{User Issues} & & Residential district \\  & & Office \\  & & High School \\  & User scenarios & Commercial Street \\  & & Subway \\  & & Rural \\  & & High Speed Rail \\ \hline \multirow{8}{*}{User Calls Network problems during user calls} & & No mobile phone signal \\  & & Can’t get through with signal \\  & & Sudden interruption during the call \\  & & Noise, inaudible, intermittent calls \\  & & Crosstalk \\  & & One party cannot be heard during the call \\ \hline \multirow{8}{*}{Networks, costs and remaining issues} & & Whether encountered network problems \\  & & Off-network \\  & & Poor mos quality \\  & & Number of missed calls \\ \hline \multirow{8}{*}{Networks, costs and remaining issues} & & Voice mode \\  & & Client Star Rating \\  & & 4/5G users \\ \cline{1-1}  & Remaining issues & Whether 4G network customer (local \\  & & excluding IoT) \\ \cline{1-1}  & & Whether 5G network customer \\ \cline{1-1}  & & Voice Call—Duration (minutes) \\ \hline \multirow{8}{*}{Cost issues} & & Amount in arrears for the current month \\ \cline{1-1}  & & Amount in arrears for the previous 3 months \\ \cline{1-1}  & & ARPU for the current month \\ \cline{1-1}  & & Current Month MOU \\ \cline{1-1}  & & Previous 3 Months MOU \\ \hline \end{tabular}
\end{table} 
Table 3: Initial scoring and evaluation system of voice service user. The results of the KMO test show that the value of KMO is 0.807. Meanwhile, the results of [PERSON]'s spherical test show that the significance \(p\)-value is 0.0002 \(<\) 0.05, which shows significance at the level, rejecting the original hypothesis that there is a correlation between the variables and that the factor analysis is valid to the extent of suitability. 3. Determining the number of principal factors. Firstly, the sample covariance matrix with some elements is calculated. Then, \(n\) is defined as sample size, \(z_{ik}\) is zero centralization of \(z_{i}\), \(z_{ik}\) is zero centralization of \(z_{j}\), and \(s_{ij}\) is the value of the sample covariance matrix and can be expressed as  
\[s_{ij}=\frac{1}{n-1}{\sum_{k=1}}z_{ik}z_{jk} \tag{6}\]  
The eigenvalue decomposition of the sample covariance matrix \(S\) is performed to obtain p eigenvalues \(\lambda_{1}\geq\lambda_{2}\cdot\cdot\cdot\cdot\geq\lambda_{n}\). The corresponding eigenvalue vector is \(r_{1},r_{2}\cdot\cdot\cdot r_{n}\), and the eigenvectors of the first m largest eigenvalues can be taken to estimate the factor loading matrix. Meanwhile, to ensure the variance of each component of the common factor vector \(1\), it needs to be divided by the corresponding standard deviation \(\lambda_{i}\). The corresponding eigenvector \(r_{j}\) in the factor loading matrix is then multiplied by \(\lambda_{i}\). Thus, the factor loading matrix \(\hat{A}\) can be obtained as follows.