## 1 Introduction
In addition, most of these studies used the method of simulation, statistically calculated the indexes according to the comparison matrix or weight [40; 41; 42] and then evaluated the advantages and disadvantages of the system according to the comprehensive score results [43; 44]. None of these approaches takes full advantage of the historical data of network operation. These historical data, which are recorded through previous exercises and task implementation, possess real network guarantee situations and task completion effect evaluation, and the historical possess great reference value. However, the amount of historical data is very large, and the evaluation conclusion must be generated quickly in the process of network scheme design and optimization; thus, we have to convert the large amount of data into fewer and easier to calculate indicators and algorithms. Factor analysis can transform a large amount of data into a small number of independent factors. These independent factors can reflect most of the information of the original data, which is within the scope of a reasonable research idea. Factor analysis is a multivariate statistical method that extracts public factors from the evaluation data set and describes the original variables with public factors [45]. It is an objective evaluation method completely starting from the data set, which can better overcome the influence of personnel's subjective consciousness and improve the objectivity and science of the evaluation conclusion [46]. The factor analysis model is defined as a linear model showing the relationship between common factors and variables. In general, the factor analysis model is shown with Equation (1). \[ \begin{array}{l}x_{1}=a_{11}\mathrm{F}_{1}+a_{12}\mathrm{F}_{2}+\cdots+a_{ 1m}\mathrm{F}_{m}+\varepsilon_{1}\\ x_{2}=a_{21}\mathrm{F}_{1}+a_{22}\mathrm{F}_{2}+\cdots+a_{2m}\mathrm{F}_{m}+ \varepsilon_{2}\\ \cdots\\ x_{p}=a_{p1}\mathrm{F}_{1}+a_{p2}\mathrm{F}_{2}+\cdots+a_{pm}\mathrm{F}_{m}+ \varepsilon_{p}\end{array}  \tag{1}\]  
The matrix form is expressed as follows:  
\[X=A\mathrm{F}+\varepsilon \tag{2}\]  
where \(x_{1}\), \(x_{2}\)... \(x_{p}\) represents random data of \(p\) original indexes and \(\mathrm{F}_{1}\), \(\mathrm{F}_{2}\)... \(\mathrm{F}_{m}\) are the extracted common factor scores that are unobservable random variables. \(\varepsilon\) is the special factor and represents the part that cannot be explained by common factors. The coefficient \(a_{ij}\) is the factor loading and its size indicates the importance of the common factor relative to the original indexes [47]. Data dimensionality reduction cannot reduce the collection of original data but also brings in the complexity of calculation. However, the mobile communication system networking scheme needs to be evaluated quickly and recommended in the order of the decision makers' preferences. Therefore, there is an urgent need for a fast, objective and favorite-supporting network effectiveness evaluation method. The main contributions of this paper can be summarized as follows:  
* We study an effectiveness evaluation method of mobile communication network design schemes and propose a design scheme for the evaluation and optimization of network plans. * We propose an improved method of effectiveness evaluation based on factor analysis. It can not only effectively use the historical data but also greatly reduce the amount of data collection and calculation. * We propose a decision preference setting method based on cluster analysis. The rest of the paper is structured as follows: Section 2 presents the background for our paper. Section 3 introduces the traditional effectiveness evaluation method of factor analysis. Section 4 proposes an improved method of effectiveness evaluation based on factor analysis and verifies its validity by an example. Section 4 proposes a decision preference setting method based on cluster analysis. Section 5 Synthesizes the above studies and provides the detailed process of effectiveness evaluation of mobile communication network design schemes.