# Analysis of International Competitiveness of China’s Mobile Phone Industry Based on Data Mining Algorithm

Authors: Jun Lu; ZhongLi Lv
Journal: Security and Communication Networks
 (2022)

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## Abstract

In order to analyze the international competitiveness of China-made mobile phones, this paper combines data mining algorithms to analyze the international competitiveness of China’s mobile phone industry, improve the international market share of China-made mobile phones, and study the concept of multiattribute problems and related research methods. Moreover, the paper carries out the model description of the selected research methods combining principal component analysis, data envelopment analysis, and sorting method approaching the ideal solution. In addition, this paper expounds on the principles and models of PCA, DEA, and TOPSIS and selects an intelligent algorithm suitable for this model. Finally, this paper verifies the validity of the model proposed in this paper, conducts statistical analysis through the data mining model, evaluates the data mining effect through multiple simulation exercises, and verifies the validity of the system model.


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## Body

## 1. Introduction

Faced with the problems of high market share and low profit margins of domestic mobile phone companies, clustering of low-end and middle-end markets, weakness in the high-end market, and low profits, in the fierce market competition, timely adjustment of business strategies to enhance the competitiveness of domestic mobile phone companies has become a top priority. At the same time, as consumers of corporate products and services, the perceived value of customers can be said to be a new and important source of corporate competitiveness. Therefore, it is necessary to proceed from the perspective of customer value. If customers have high perceived value for a certain mobile phone product, then the company has good competitiveness. In order to gain strong competitiveness, domestic mobile phone companies should establish a customer-oriented business philosophy, enhance customer perceived value, and regularly monitor and utilize customer perceived value, so as to provide customers with mobile phone products and services with higher customer value. Therefore, thinking about how to improve the competitiveness of domestic mobile phone companies from the perspective of customer value is an important research idea.This article combines data mining algorithms to analyze the international competitiveness of China’s mobile phone industry, increase the market share of domestic mobile phones in the world, and provide a reference for the subsequent development of our country’s mobile phone industry.


## 2. Related Work

The theory of enterprise competitiveness originates from the research on the acquisition and maintenance of competitiveness. According to the origin of enterprise competitiveness, it can be divided into two major theoretical schools: exogenous theory and endogenous theory [1]. The school of exogenous theory focuses on the analysis of the external competitive environment of enterprises and believes that the competitiveness of enterprises is reflected in the comparison with competitors. Literature [2] points out that enterprises design, manufacture, and sell goods or provide services in their own environment. When compared with other competitors, the ability and opportunity that is more attractive in price and quality is the competitiveness of enterprises; literature [3] believes that compared with competitors, the ability of enterprises to acquire, create, and use knowledge is stronger or weaker. It can be characterized as the competitiveness of enterprises. Michael Porter attributed the dominant position of enterprises in the industry to the competitiveness of enterprises. Literature [4] pointed out that the core of enterprise competitiveness is comparative productivity, which itself is based on the interindividual, a relative concept obtained by comparison. When the above scholars define the competitiveness of enterprises, although they consider different angles, they all focus on the analysis of the external competitive environment of enterprises, which belongs to the school of exogenous theory. The endogenous school focuses on the analysis of the internal situation of the enterprise. Literature [5] believes that the human and material resources of the enterprise itself form the competitiveness of the sustainable and long-term development of the enterprise. Literature [6] attributes the competitiveness of the enterprise to the interior of the enterprise. Domestic scholars also hold similar views. Literature [7] believes that enterprise competitiveness is the ability to face the market and customers.In the exploration of the connotation of technological competitiveness of enterprises from different perspectives, the views of various scholars have not been unified, but they can be basically divided into two schools: the ability school and the resource school. With the deepening of research studies, the literature [8] integrated the viewpoints of the capability school and the resource school, believed that the enterprise itself is a combination of resources and capabilities, and pointed out that the technological competitiveness of enterprises stems from the full use of internal and external technological innovation resources, especially the full use of internal and external technological innovation resources. The role of the unique, scarce, and irreplaceable technological assets owned by an enterprise enables itself to provide more attractive products or services in the market and to obtain more long-term benefits than its competitors. By evaluating the technological competitiveness of an enterprise, it can promote the efficient and rational use of all its own resources and maximize the ability of the enterprise. With the passage of time, the importance of technology in the development of enterprises, especially high-tech enterprises, has become increasingly significant. If an enterprise wants to surpass or defeat its competitors, it must improve its technological competitiveness [9].Literature [10] comprehensively uses the analytic hierarchy process, the entropy weight coefficient method, the Delphi method, and the regression analysis method to evaluate the technological competitiveness of our country’s self-owned brand automobile enterprises with the technological input and output capabilities of enterprises as the breakthrough point. Literature [11] verified the uniqueness and applicability of the new evaluation theory of cross analysis and the corresponding cross-analysis method for the evaluation of enterprise technological competitiveness through empirical research and provided new research ideas and research methods for the study of enterprise technological competitiveness. Literature [12] refines the technological competitiveness of enterprises according to the three main stages of technology research and development, technology integration, and technology monopoly in the process of independent innovation and establishes a structural equation model based on the path relationship between strategic orientation, technological competitiveness, and enterprise status. Literature [13] designed the evaluation index of IoT technology competitiveness based on the “push-pull model” and “human technology symbiosis model.” Literature [14] uses the AHP, based on the patent-based LED enterprise technology competitiveness evaluation system, analyzes the innovation input and output data of LED packaging listed enterprises, and evaluates the enterprise’s technological competitiveness. Literature [15] uses the relative technical advantages of patents to define the technical strength of each research object and draws a patent portfolio chart with the technical attractiveness of each company’s mainstream technology, the relative position of patents, and technical strength as indicators. Literature [16] emphasizes that brands have both functional value and emotional value.


## 3. Data Mining Algorithm Based on Principal Component Analysis

In data analysis, we often face the problems of judging the pros and cons of things and mutual rules. However, the factors that affect the characteristics of a certain thing and its development law are diversified. In order to analyze more deeply, we need to analyze and evaluate various influencing factors related to it. However, multivariate and large sample data will bring about problems such as multicollinearity, and the duplication of information reflected by each influencing factor will affect the authenticity and scientificity of the statistical results. Therefore, in order to avoid information overlap and reduce workload as much as possible, people put forward the idea of “dimensionality reduction.” Principal component analysis is performed by finding a few uncorrelated variables, calculating their linear combination, and transforming them into a few comprehensive indicators. At the same time, it saves most of the information in the original variable data, which is the most widely used multivariate statistical analysis method.The principal components are several comprehensive indexes formed by the original indexes through a series of mathematical calculations, that is,F1, F2, and F3 in the above example. According to the amount of information contained in the principal components, they are called “first principal component,” “second principal component,” “third principal component,” and so on.


### 3.1. Mathematical Model of Principal Components

X1,X2,X3,…,Xp is a p-dimensional random variable, and principal component analysis is done to transform p observed variables into p new indicators through linear combination, namely,(1)F1=μ11X1+μ12X2+⋯+μ1pXp,F2=μ21X1+μ22X2+⋯+μ2pXp,M,Fp=μp1X1+μp2X2+⋯+μppXp.The model meets the following conditions:(1)
μi12+μi22+⋯+μip2=1(2)
CovFi,Fj=0,i≠j,i,j=1,2,…,p(3)
VarF1≫VarF2≫≫VarFPThe purpose of principal component analysis is to simplify variables, and the number of principal components is usually less than the number of original variables. However, as for the actual problems, several principal components should be retained. We naturally hope that the principal components reflect as much information of the original variables as possible. Therefore, it is necessary to weigh the number of principal components and the retained information. The “information” here is measured by variance; that is, the largerVarF1 is, the more information F1 contains. In the end, the new indicators F1, F2, F3, … Fkk≤p fully retain the main information in the original variables and are independent of each other.


### 3.2. Principal Component Analysis

The specific steps of principal component analysis are as follows:(1)
The algorithm calculates the correlation coefficient matrix.(2)
The algorithm calculates the characteristic root of the correlation coefficient matrix and the corresponding characteristic vector.(3)
The algorithm selects the largest feature root, and the corresponding feature vector is equal to the coefficient of the first principal component. Moreover, the algorithm selects the second largest feature root, and the corresponding feature vector is equal to the coefficient of the second principal component.(4)
The algorithm calculates the cumulative contribution rate and selects the appropriate number of principal components.(5)
The algorithm writes the expressions of the firstk principal components.Data envelopment analysis is widely used in operations research, management science, and economics. In the production activities of enterprises, due to limited resources, producers always want to get the most output with the least input. From the perspective of input and output, the DEA method is a new input-output dual-criteria model, which minimizes input while ensuring maximum output. The DEA method is a nonparametric estimation method, which is suitable for dealing with problems that require decision-making or evaluation indicators, and the goal itself does not have to have a clear functional form. Specifically, the DEA method, based on linear programming theory, performs a series of mathematical calculations on input index and output index data and is a quantitative analysis method that evaluates the relative effectiveness of decision-making cases with the same attributes.The DEA method mainly evaluates the relative efficiency of the decision-making unit (DMU). Each DMU has the same input variables and output variables. The DEA method obtains the comprehensive efficiency index of each DMU by calculating the weighted ratio of output and input data and ranks the DMUs according to this value to determine the effective DMU, which is the decision-making unit with the highest relative efficiency.Next, we will introduce the classic DEA model: theC2R model. We assume that there are n decision-making units and each decision-making unit has m kinds of input variables and s kinds of output variables, which, respectively, represent the “resources consumed” and “effects of work” of the decision-making unit, in Figure 1.Figure 1
Input and output of the decision-making unit.Now, we want to evaluate the efficiency of thei0-th decision-making unit. For the convenience of operation, DMUi is abbreviated as DMU0,Xi,Yi as X0,Y0,andh6 as h0. The classic C2R model optimizes the weight coefficients u and v to maximize h0, which is used to solve the following optimization problem:(2)maxh0=∑r=1sμryr0∑j=1nvjxj0,∑r=1sμryri∑j=1nvjxji≤1,i=1,…,n,ur>0,r=1,…,s,vj>0,j=1,…,m.Here,yri is the output of the i-th decision-making unit for the r-th type of output indicator and xji is the i-th decision-making unit’s input for the j-th type of input indicator. ur and vj are the weight coefficients corresponding to the optimal solution of the above-mentioned maximum problem.In the above-mentioned optimization problem, for eachh0∗, we can obtain a set of optimal μr∗andvj∗. If the maximum efficiency ratio among them reaches 1, then we call the decision unit to be technically effective. A decision-making unit that is technically effective means that it does not need to increase any output or reduce any input. The closer the relative efficiency value is to 1, the higher the energy consumption efficiency of the decision-making unit.We set as follows:(3)t=1∑j=1mvjxj0,ω=tv,μ=tμ.Then, the original fractional planning can be transformed into(4)max∑r=1sμryr0,∑j=1mωjxji−∑r=1sμryri≥0,i=1,2,…,n,∑j=1mωjxj0=1,μr≥0,r=1,2,…,s,ωj≥0,j=1,2,…,m.The dual problem of linear programming is as follows:(5)minθ,∑i=1nXiλi+s−=θX0,λi≥0;i=1,2,…,n;s+≥0,s−≥0.Here,s+ands− are the slack variables.The above is the mathematical model of the DEA method. The biggest advantage of this method is it being simple and easy to understand. In practical applications, the algorithm is also relatively easy to implement. It can be solved by special DEAP2.0 software or by writing a Matlab program. However, the DEA method also has some shortcomings in solving practical problems. We usually require that the number of decision-making units should not be less than twice the output index, and it is also necessary to ensure that the data are nonnegative. In addition, if there is a large correlation between input variables and output variables, DEA’s evaluation results will be affected. This can be solved ingeniously by combining this with PCA. Therefore, before using DEA for efficiency evaluation, this paper first uses PCA to reduce the dimensionality of the indicators and replaces all the original indicators with a few principal components.In fact, the DEA method only obtains the calculation results of relative efficiency, that is, determines which decision-making units are relatively effective or uses resources relatively efficiently, but cannot obtain the detailed characteristics and properties of the data of these effective units and other information. That is to say, just calculating the utilization efficiency of resource input and output using decision-making units cannot fundamentally explain the reasons for selecting certain decision-making unit gap between alternatives. In order to solve this problem, this paper further analyzes the results calculated by the DEA method through the TOPSIS method and studies the preference value of the data contained in all decision-making units, so as to make a more comprehensive assessment of the comprehensive competitiveness of each cruise home port.The sorting method approximates the ideal solution (technique for preference by similarity to the ideal solution, abbreviated as TOPSIS). The preference value can be converted into Euclidean distance for calculation and measurement, and after comprehensive comparison, an optimal plan and the comparison of all plans can be obtained.The basic idea of the TOPSIS method is to consider the distance between the candidate scheme and the most ideal scheme and the least ideal scheme at the same time. However, there is usually no such positive ideal solution and negative ideal solution in the original scheme set. Therefore, if there is a solution in the scheme that is both closest to the positive ideal solution and farthest from the negative ideal solution, this solution is the best solution in the scheme set plan; otherwise, it is the worst plan. When applying the TOPSIS method, it is necessary to determine the weight of an evaluation index. Scholars at home and abroad are actively studying scientific and reasonable weight value determination methods to solve practical problems, and now, they have achieved good results.The calculation steps of the TOPSIS method are described as follows:(1)
First, the algorithm converts the original data into a decision matrix. The decision matrix is defined as follows:(6)C=x11⋯x1n⋮⋱⋮xm1⋯xmn.Here,m is the number of alternatives, n is the number of evaluation indexes, and xij is the evaluation value of the j-th index of the i-th program.(2)
Since the dimensions of the selected evaluation indicators are different, in order to facilitate comparison, we need to standardize the following initial indicator data:R=rijm×n, where rij=xij/∑ixij2,i=1,…,m;j=1,…,n.(3)
The algorithm can get the weight value of each evaluation index (attribute)Wjj=1,…,n. Next, the algorithm defines the weighted standardized decision matrix as V, and the calculation of V=R×W is as follows:(7)V=v11⋯v1n⋮⋱⋮vm1⋯vmn.Here,vij=rij×wjand∑j=1nwj=1.(4)
The algorithm determines the positive ideal solutionA+ and the negative ideal solution A−.(8)Positive ideal solution:A+=v1+,v2+,…,vj+,…,vn+,negative ideal solution:A−=v1−,v2−,…,vj−,…,vn−,wherevj+=maxvij,j∈J+minvij,j∈J−j=1,2,…,n,vj−=maxvij,j∈J−minvij,j∈J+j=1,2,…,n.J+ is the benefit-type attribute collection and J− is the cost attribute collection.(5)
The algorithm calculates the distance. Here, we use the Euclidean distance to calculate the distance from each alternative to the positive ideal solutionA+ and the negative ideal solution A−, respectively.(9)Si+=∑j=1nvij−vj+2i=1,2,…,m,Si−=∑j=1nvij−vj−2i=1,2,…,m.The algorithm calculates the relative closeness to the positive ideal solutionA+ and the relative closeness is as follows:(10)Ci=Si−Si++Si−.(6)
The algorithm arranges the preference order, sorts the alternatives according to the descending order of the value ofCi, and chooses the one with the largest value of Ci as the optimal scheme.After the above steps, we can apply the TOPSIS method to select the best scheme from a large number of alternative schemes and, at the same time, sort all the schemes by preference value.


## 3.1. Mathematical Model of Principal Components

X1,X2,X3,…,Xp is a p-dimensional random variable, and principal component analysis is done to transform p observed variables into p new indicators through linear combination, namely,(1)F1=μ11X1+μ12X2+⋯+μ1pXp,F2=μ21X1+μ22X2+⋯+μ2pXp,M,Fp=μp1X1+μp2X2+⋯+μppXp.The model meets the following conditions:(1)
μi12+μi22+⋯+μip2=1(2)
CovFi,Fj=0,i≠j,i,j=1,2,…,p(3)
VarF1≫VarF2≫≫VarFPThe purpose of principal component analysis is to simplify variables, and the number of principal components is usually less than the number of original variables. However, as for the actual problems, several principal components should be retained. We naturally hope that the principal components reflect as much information of the original variables as possible. Therefore, it is necessary to weigh the number of principal components and the retained information. The “information” here is measured by variance; that is, the largerVarF1 is, the more information F1 contains. In the end, the new indicators F1, F2, F3, … Fkk≤p fully retain the main information in the original variables and are independent of each other.


## 3.2. Principal Component Analysis

The specific steps of principal component analysis are as follows:(1)
The algorithm calculates the correlation coefficient matrix.(2)
The algorithm calculates the characteristic root of the correlation coefficient matrix and the corresponding characteristic vector.(3)
The algorithm selects the largest feature root, and the corresponding feature vector is equal to the coefficient of the first principal component. Moreover, the algorithm selects the second largest feature root, and the corresponding feature vector is equal to the coefficient of the second principal component.(4)
The algorithm calculates the cumulative contribution rate and selects the appropriate number of principal components.(5)
The algorithm writes the expressions of the firstk principal components.Data envelopment analysis is widely used in operations research, management science, and economics. In the production activities of enterprises, due to limited resources, producers always want to get the most output with the least input. From the perspective of input and output, the DEA method is a new input-output dual-criteria model, which minimizes input while ensuring maximum output. The DEA method is a nonparametric estimation method, which is suitable for dealing with problems that require decision-making or evaluation indicators, and the goal itself does not have to have a clear functional form. Specifically, the DEA method, based on linear programming theory, performs a series of mathematical calculations on input index and output index data and is a quantitative analysis method that evaluates the relative effectiveness of decision-making cases with the same attributes.The DEA method mainly evaluates the relative efficiency of the decision-making unit (DMU). Each DMU has the same input variables and output variables. The DEA method obtains the comprehensive efficiency index of each DMU by calculating the weighted ratio of output and input data and ranks the DMUs according to this value to determine the effective DMU, which is the decision-making unit with the highest relative efficiency.Next, we will introduce the classic DEA model: theC2R model. We assume that there are n decision-making units and each decision-making unit has m kinds of input variables and s kinds of output variables, which, respectively, represent the “resources consumed” and “effects of work” of the decision-making unit, in Figure 1.Figure 1
Input and output of the decision-making unit.Now, we want to evaluate the efficiency of thei0-th decision-making unit. For the convenience of operation, DMUi is abbreviated as DMU0,Xi,Yi as X0,Y0,andh6 as h0. The classic C2R model optimizes the weight coefficients u and v to maximize h0, which is used to solve the following optimization problem:(2)maxh0=∑r=1sμryr0∑j=1nvjxj0,∑r=1sμryri∑j=1nvjxji≤1,i=1,…,n,ur>0,r=1,…,s,vj>0,j=1,…,m.Here,yri is the output of the i-th decision-making unit for the r-th type of output indicator and xji is the i-th decision-making unit’s input for the j-th type of input indicator. ur and vj are the weight coefficients corresponding to the optimal solution of the above-mentioned maximum problem.In the above-mentioned optimization problem, for eachh0∗, we can obtain a set of optimal μr∗andvj∗. If the maximum efficiency ratio among them reaches 1, then we call the decision unit to be technically effective. A decision-making unit that is technically effective means that it does not need to increase any output or reduce any input. The closer the relative efficiency value is to 1, the higher the energy consumption efficiency of the decision-making unit.We set as follows:(3)t=1∑j=1mvjxj0,ω=tv,μ=tμ.Then, the original fractional planning can be transformed into(4)max∑r=1sμryr0,∑j=1mωjxji−∑r=1sμryri≥0,i=1,2,…,n,∑j=1mωjxj0=1,μr≥0,r=1,2,…,s,ωj≥0,j=1,2,…,m.The dual problem of linear programming is as follows:(5)minθ,∑i=1nXiλi+s−=θX0,λi≥0;i=1,2,…,n;s+≥0,s−≥0.Here,s+ands− are the slack variables.The above is the mathematical model of the DEA method. The biggest advantage of this method is it being simple and easy to understand. In practical applications, the algorithm is also relatively easy to implement. It can be solved by special DEAP2.0 software or by writing a Matlab program. However, the DEA method also has some shortcomings in solving practical problems. We usually require that the number of decision-making units should not be less than twice the output index, and it is also necessary to ensure that the data are nonnegative. In addition, if there is a large correlation between input variables and output variables, DEA’s evaluation results will be affected. This can be solved ingeniously by combining this with PCA. Therefore, before using DEA for efficiency evaluation, this paper first uses PCA to reduce the dimensionality of the indicators and replaces all the original indicators with a few principal components.In fact, the DEA method only obtains the calculation results of relative efficiency, that is, determines which decision-making units are relatively effective or uses resources relatively efficiently, but cannot obtain the detailed characteristics and properties of the data of these effective units and other information. That is to say, just calculating the utilization efficiency of resource input and output using decision-making units cannot fundamentally explain the reasons for selecting certain decision-making unit gap between alternatives. In order to solve this problem, this paper further analyzes the results calculated by the DEA method through the TOPSIS method and studies the preference value of the data contained in all decision-making units, so as to make a more comprehensive assessment of the comprehensive competitiveness of each cruise home port.The sorting method approximates the ideal solution (technique for preference by similarity to the ideal solution, abbreviated as TOPSIS). The preference value can be converted into Euclidean distance for calculation and measurement, and after comprehensive comparison, an optimal plan and the comparison of all plans can be obtained.The basic idea of the TOPSIS method is to consider the distance between the candidate scheme and the most ideal scheme and the least ideal scheme at the same time. However, there is usually no such positive ideal solution and negative ideal solution in the original scheme set. Therefore, if there is a solution in the scheme that is both closest to the positive ideal solution and farthest from the negative ideal solution, this solution is the best solution in the scheme set plan; otherwise, it is the worst plan. When applying the TOPSIS method, it is necessary to determine the weight of an evaluation index. Scholars at home and abroad are actively studying scientific and reasonable weight value determination methods to solve practical problems, and now, they have achieved good results.The calculation steps of the TOPSIS method are described as follows:(1)
First, the algorithm converts the original data into a decision matrix. The decision matrix is defined as follows:(6)C=x11⋯x1n⋮⋱⋮xm1⋯xmn.Here,m is the number of alternatives, n is the number of evaluation indexes, and xij is the evaluation value of the j-th index of the i-th program.(2)
Since the dimensions of the selected evaluation indicators are different, in order to facilitate comparison, we need to standardize the following initial indicator data:R=rijm×n, where rij=xij/∑ixij2,i=1,…,m;j=1,…,n.(3)
The algorithm can get the weight value of each evaluation index (attribute)Wjj=1,…,n. Next, the algorithm defines the weighted standardized decision matrix as V, and the calculation of V=R×W is as follows:(7)V=v11⋯v1n⋮⋱⋮vm1⋯vmn.Here,vij=rij×wjand∑j=1nwj=1.(4)
The algorithm determines the positive ideal solutionA+ and the negative ideal solution A−.(8)Positive ideal solution:A+=v1+,v2+,…,vj+,…,vn+,negative ideal solution:A−=v1−,v2−,…,vj−,…,vn−,wherevj+=maxvij,j∈J+minvij,j∈J−j=1,2,…,n,vj−=maxvij,j∈J−minvij,j∈J+j=1,2,…,n.J+ is the benefit-type attribute collection and J− is the cost attribute collection.(5)
The algorithm calculates the distance. Here, we use the Euclidean distance to calculate the distance from each alternative to the positive ideal solutionA+ and the negative ideal solution A−, respectively.(9)Si+=∑j=1nvij−vj+2i=1,2,…,m,Si−=∑j=1nvij−vj−2i=1,2,…,m.The algorithm calculates the relative closeness to the positive ideal solutionA+ and the relative closeness is as follows:(10)Ci=Si−Si++Si−.(6)
The algorithm arranges the preference order, sorts the alternatives according to the descending order of the value ofCi, and chooses the one with the largest value of Ci as the optimal scheme.After the above steps, we can apply the TOPSIS method to select the best scheme from a large number of alternative schemes and, at the same time, sort all the schemes by preference value.


## 4. Analysis of International Competitiveness of China’s Mobile Phone Industry Based on Data Mining Algorithms

Figure2 intuitively shows the relationship among the competitiveness of mobile phones, the competitiveness of the mobile phone industry, and the competitiveness of mobile phone companies.Figure 2
The relationship between national competitiveness, industrial competitiveness, and enterprise competitiveness.Figure2 shows that corporate competitiveness, industrial competitiveness, and national competitiveness are in a progressive relationship, and the former cannot be simply summed up to get the latter. The relationship between the three is like a pyramid, and the bottom-up shows a clustering effect. In other words, the advantages of the lower layer can be summarized and refined to the upper layer to influence the upper layer. From the top to bottom, it shows a penetration effect; that is, the policies of the upper level will act on the lower level.The determinants of the competitive advantage of a country, industry, or enterprise can be summarized as four basic determinants and two auxiliary factors. The four basic determinants are production factors, demand conditions, related and supporting industries, and corporate strategy, structure, and competition in the same industry. The two supporting factors are government and opportunity. Each factor has far-reaching significance, and different factors are related to each other, as shown in Figure3.Figure 3
Diamond model.The three perspectives of environmental competitiveness, enterprise competitiveness, and market competitiveness are used to evaluate the international competitiveness of the industry. Environmental competitiveness is the basis for the competitiveness of an industry, including the country’s industrial policy and industrial layout, domestic demand conditions, and foreign direct investment. Whether these factors have advantages directly determines whether the industry has international competitiveness. The competitiveness of an enterprise is the core of whether an industry is competitive, including the economies of scale of the enterprise, the labor productivity of its employees, and the R&D capabilities. As a microsubject, an enterprise can rationally use resource endowments and realize the optimal allocation of resources, which is a decisive condition for an enterprise to have a competitive advantage. Market competitiveness is a supplementary condition for whether an industry is competitive, including revealed comparative advantage index (RCA), international market share, and trade competitiveness index. These factors can help the mobile phone industry to expand its competitiveness. Similarly, the lack of market competitiveness will directly lead to the low competitiveness of the industry, as shown in Figure4.Figure 4
Evaluation method of the international competitiveness of the mobile phone industry.After years of development, China’s mobile phone industry has formed a multidimensional and interactive industrial chain. Due to the different expression methods of different experts, based on the opinions of other experts and scholars, this article summarizes and describes China’s mobile phone industry chain as shown in Figure5:Figure 5
Schematic diagram of China’s mobile phone industry chain.The activities of the value chain are independent of each other, but they are also connected. It classifies the value activities of enterprises according to assets, personnel, functions, and values. It is necessary to well identify the basic activities and auxiliary activities, production and operation, sales, and services. Sometimes, a basic value chain can also be decomposed again, so the various activities of the enterprise must be strictly distinguished, neither overlapping nor missing. The value chain of a mobile phone company can be expressed as shown in Figure6.Figure 6
The value chain diagram of mobile phone companies.The life cycle characteristics of mobile phone products can be represented by the change trend of the product value in the R&D phase, product investment period, growth period, maturity period, and decline period, which can be further illustrated in Figure7. In the figure, the AB stage represents the initial development stage of the product. It can be seen that the value generated by the product at this stage is negative, the company is in the preassessment stage of the product, and there is only input and no corresponding output. BC represents the input period of the product. It has a small batch of samples output from the acquisition of the hard mold, so its curve shows an upward trend, which is close to the X axis. CD represents the growth period of the product. During this period, the demand for products has increased rapidly and it has begun to enter the stage of mass production. Since the products in this period just entered the market, the price of the products was relatively high, so the value of output increased significantly and showed a close-to-straight upward trend.Figure 7
Life cycle characteristics of mobile phone products.Combined with the above model analysis, this article combines the third part of the algorithm to build a data mining system. Data mining is a process of extracting valuable information and data from a large amount of data. A large amount of historical data is generated in various operation links, and these data are an intangible asset of the enterprise. Which company can effectively and deeply mine the value of data can better provide customers with efficient products or services. Because different industries use different analytical techniques and tools, the analytical methods can be unified and coordinated to solve many valuable business problems. The data mining model proposed in this paper is shown in Figure8.Figure 8
Flow chart of data mining modeling.On the basis of the above research, the effectiveness of the model in this paper is verified, the data mining model is used for statistical analysis, the data mining effect is evaluated through multiple simulation exercises, and the results shown in Figure9 are obtained.Figure 9
Cluster evaluation diagram of data mining effect.This paper evaluates the clustering effect of the system constructed in this paper in the analysis of the international competitiveness of the mobile phone industry, and the results shown in Figure10 are obtained.Figure 10
The clustering effect of the system in the analysis of the international competitiveness of the mobile phone industry.Through cluster analysis, the industrial chain should be integrated to enhance the comprehensive competitiveness of local enterprises. It is necessary to strengthen cooperation between domestic mobile phone companies, achieve group breakthroughs, carry out industrial upgrades, and jointly promote the healthy development of domestic mobile communication terminal products with the power of groups. Moreover, it is necessary to strengthen the cooperation between domestic mobile phone companies and mainstream mobile phone design companies to make up for the inherent shortcomings of domestic mobile phone back-ends (independent research and development of core technologies, etc.), so that R&D products can maintain a close tracking advantage with international brands. At the same time, it is necessary to strengthen the strategic cooperation between domestic mobile phone companies and operators to reduce the risks of mobile phone production and to diversify business projects with the help of various value-added services of operators, expand the market, and seek new profit growth points. It is necessary to systematically strengthen brand building, clarify product positioning, enhance brand value, and avoid excessive price competition under homogenization. It is necessary to deeply tap the potential of the third and fourth tier markets, complete the accumulation of enterprise strength, prepare for the first and second markets, actively explore foreign markets, highlight the encirclement, and reverse the situation with exports. It is necessary to strengthen the internal management of domestic mobile phone companies to ensure the implementation of the system. In terms of enhancing core competitiveness, it is necessary to increase technological research and development, introduce advanced design technology and production technology, and carry out secondary development on the basis of mastering core design technology and production technology. Finally, the direction of research and development needs to be adjusted. Domestic mobile phone companies should seize the opportunity of upgrading the 5G industry, accelerate the research and development, tracking, technical reserves, and other preparations for the third generation of mobile communications, master core technology as soon as possible, and completely change China’s passive situation of being completely controlled by others in the global mobile phone industry chain.


## 5. Conclusion

As consumers’ understanding of mobile phones continues to increase, they have become very rational in buying mobile phones and they have paid much attention to the value of products and services provided by enterprises. By consulting the related literature, this article combines the current status of China’s mobile phone market research to identify the customer value evaluation indicators of mobile phone companies and then evaluates and scores various indicators that drive customer value through questionnaire research. Moreover, this paper assigns a certain weight to each indicator based on the data of the questionnaire survey, thereby calculating the customer perceived value of mobile phone companies in the Chinese market. In addition, this paper conducts a comparative analysis to obtain the comparative competitive advantages and disadvantages of various mobile phone companies and then proposes corresponding customer value creation strategies based on this. These have very important practical guiding significance for enhancing the competitiveness of domestic mobile phone companies. This article combines data mining algorithms to analyze the international competitiveness of China’s mobile phone industry, increase the international market share of domestic mobile phones, and provide a reference for the subsequent development of China’s mobile phone industry.



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*Source: 8460574-2022-04-23.xml*