# Research on the Financial Support Performance Evaluation of Big Data Industry Development

Authors: Wenyu Fu; Jingfeng Zhao; Changming Wang; Xiaobin Zhou
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

In order to solve the problem that the traditional information systems have such as slow running responses and cannot meet the expected requirements of users, a performance evaluation method of financial support for the development of the big data industry is proposed. According to the characteristics of financial support, this paper uses the analytic hierarchy process (AHP) and the fuzzy comprehensive evaluation principle to establish a specific evaluation index system for the performance evaluation of financial support in the big data industry and select a suitable index group. In order to reflect the financial support performance, the analytic hierarchy process (AHP) is used to calculate the weight of various performance evaluation indicators, and the fuzzy comprehensive evaluation is used to combine qualitative analysis and quantitative analysis to evaluate and calculate the big data industry scientifically, objectively, fairly, and accurately. In financial support performance, experiments show that the proposed method not only has a fast response time but also can ensure that the actual results after the system runs are in line with the expectations of the system users.


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## Body

## 1. Introduction

The big data industry is an economic set related to all links of data, such as its production, storage, analysis, processing, and application, and it is becoming a new growth point of the future economy and an important factor to promote economic structure transformation and industrial upgrading, leading to a new pattern of economic and social development. Adhering to the innovation-driven development, accelerating the deployment of big data and deepening the big data applications have become the intrinsic need and inevitable choice of stabilizing the growth, promoting the reform, adjusting the structure, benefiting the people, and driving the modernization of government governance ability. In order to keep up with the development pace of the world’s big data, in recent years, the Chinese government has begun to pay attention to the research and development of the big data industry, with an investment exceeding 50% in the big data industry. The State Council of the People’s Republic of China, the National Development and Reform Commission of the People’s Republic of China, the Ministry of Industry and Information Technology of the People’s Republic of China, and so on are steadily promoting the development of the big data industry by means of targeted support, project support, and so on[1].In the era of big data, by building a financial sharing center, the accounting processing efficiency can be improved. After the financial sharing center is established, an effective performance evaluation system can enable the management and employees of the financial sharing center to clarify their responsibilities and goals to ensure financial sharing. The center provides high-quality services for customers and at the same time promotes the continuous optimization of the process and improves the overall performance level. The article proposes a method to improve financial support from the aspects of clarifying performance evaluation goals, establishing performance evaluation indicators in the big data environment, improving information integration capabilities, and implementing performance evaluation work. The performance evaluation system promotes the healthy and orderly development of the financial sharing center. Big data is an interdisciplinary field, and the big data industry stretches across education, transportation, consumption, electricity, energy, health, finance, and other fields, and as an emerging industry with huge strategic significance, it should be supported and helped by finance in the early stages of development. Therefore, it is necessary for us to increase our financial investment in the big data industry, improve the infrastructure of the industry, and promote the healthy and rapid development of the big data industry [2–4].


## 2. Literature Review

There are few research studies on the financial support of the big data industry by scholars at home and abroad, but since the big data industry belongs to the emerging high-tech industry, the research on financial support for the high-tech industry’s development can also be applied to the big data industry. This paper summarizes and sorts out the existing domestic and foreign literature mainly on two aspects: the influence of financial support and the performance evaluation of financial expenditure.Some scholars have found through research studies that the effect of financial subsidies and preferential tax policies is obvious, and these research studies mainly focus on the incentive effect on enterprise R&D activities. Bloom et al. carried out comparative analysis on the economic data of eight countries in the OECD and believed that the preferential tax policies had a promoting effect on the improvement of enterprise R&D investment [5–7]. The economists, Rosa, Hanel, and Czarnitzki et al., researched Canadian companies and believed that the tax credit policy for enterprise R&D activities not only stimulates enterprises to increase R&D investments but also improves the output and profit of enterprise R&D activities. Kohler et al. believed that tax preferences or financial subsidies can promote the research and development of enterprises. Through researching the financial policies that play a major role at different stages, Deng Ziji found that the financial subsidy policy had a certain incentive effect at all stages of technological innovation, including the research and development stage and the achievement transformation stage. However, by researching, other scholars believed that financial subsidies and preferential tax policies had no effect or even had negative effects. Jaffe believed that financial subsidies or preferential tax policies had an inhibitory effect on the research and development of enterprises. Guellec and van Pottelsberghe analyzed the panel data of 17 countries in the OECD and found that the price elasticity of tax preference to enterprise R&D expenditure was negative. Tzelepis and Skuras researched and found that financial subsidies implemented for enterprise investment cannot improve the operation efficiency and profitability [8–10].In terms of financial expenditure performance evaluation, Drongelen and Cook pointed out that the performance evaluation system of government financial investment in science and technology should take the dynamic change characteristic of the requirements of stakeholders into consideration, and the key points of the performance evaluation at all stages should be different to some extent. Sohn, YongGyuJoo, and HongKyuHan used the SEM model to study the Science and Technology Promotion Fund of South Korea from three aspects, and Toshiyuki Sueyoshi et al. used the method of data envelopment analysis (DEA) to evaluate and compare the importance of R&D expenditures on Japan’s IT enterprises and other manufacturing enterprises. Luo and Chen et al. used the DEA method to establish the financial investment performance evaluation index system and its main index. The science and technology output, which included scientific and technological achievements, economic benefits, and social effects, and the system were verified by taking the performance of local financial investment in science and technology as an example. Qin combined the AHP method and the variation coefficient weighting method, used manpower and financial investment as investment indexes, and used the output of scientific and technological achievements, output of economic benefits, and output of social benefits as the output indexes to build the science and technology performance evaluation model to analyze the financial expenditure performance [11, 12]. Liu built a specific framework for financial data management that accelerates the innovation process of modern enterprises’ financial management models and focusses on the advantages of using the financial sharing center for enterprises and five specific processes for cost control, hoping to update people’s perception of enterprises and traditional concepts of financial management [13].


## 3. Establishment of the Fuzzy Hierarchy Model

It is a complicated and systematic engineering to fully, accurately, objectively, and fairly carry out the comprehensive evaluation of financial expenditure performance, and it is necessary to establish the advanced, optimized, simple, and practical data model and rely on scientific decision method [14]. Guided by the theory of performance evaluation, this paper starts from the construction of the financial support performance evaluation index system of the big data industry, establishes a proper performance evaluation index system, discusses the basis and method for the determination of evaluation index standard value, and puts forward the comprehensive application of the analytic hierarchy process (AHP) method and fuzzy comprehensive evaluation method to make the empirical evaluation in order to solve the problem of fuzzy evaluation of qualitative indexes.Using the FAHP method to carry out the quantitative evaluation is not only beneficial for making a comprehensive and objective evaluation of the efficiency of financial expenditure, reflecting the influence of various factors on the performance from different perspectives but also convenient for guiding and standardizing the management of financial expenditure in China. In view of the characteristics of nonlinearity and fuzziness in the process of the financial support performance evaluation of big data industries, this paper intends to construct several evaluation indexes, use FAHP (fuzzy analytic hierarchy process) to determine the index weight, calculate the composite score of the financial support performance, and finally obtain the quantitative and specific evaluation.


### 3.1. Evaluation Index

The diversity of big data output performance determines the multiobjective feature of its evaluation, so the financial support performance evaluation system of the big data industry should be a multiobjective evaluation system. The evaluation system can be constructed according to the idea of an analytic hierarchy process, reasonably choosing and setting the index system by hierarchy, attribute, quantity, and quality, and using the performance evaluation as the core. The evaluation index system is divided into the target hierarchy (financial investment performance), the criterion hierarchy (describing main aspects of the performance), and the index hierarchy (that can reflect the quantitative and qualitative indexes of the elements’ characteristics).According to the characteristics of financial support in the big data industry, this paper chooses three major categories of indexes, namely, financial investment, financial implementation, and financial profit, to reflect the financial support performance of the big data industry.The financial investment mainly reflects the government’s degree of emphasis on the investment in the big data industry. This paper selects four indexes to describe it from the perspective of investment, and the four indexes are the proportion of financial investments in the total financial amount, the total number of invested personnel, the structure of invested personnel, and the invested material resources.The financial implementation mainly reflects the execution status in actual implementation of the government’s financial support for the big data industry. There are four secondary indexes, and they are, respectively, the completion rate of a financial investment fund plan, the financial capital management system, the completion status of the technical indexes, and the profit-tax rate of financial investment.The financial profit mainly reflects the economic and social benefits brought by the development of the financially supported big data industry. This paper chooses four secondary indexes to measure it, and they are the output rate of financial investment; the application conversion rate of big data achievements; the per-capita number of published articles of the invested personnel; and the per-capita number of obtained patents of the invested personnel.


### 3.2. Construction of Fuzzy Hierarchy Model

The fuzzy hierarchy model is constructed according to the evaluation index system, in which the criterion hierarchy of financial support performance evaluation index system of the big data industry has three elements, and the index hierarchy has 12 indexes. The evaluation content and scope of each level are shown in Table1:Table 1
Financial support performance evaluation index system of big data industry.
Target hierarchyCriterion hierarchyIndex hierarchyConcrete meaningFinancial support performance index evaluation system of the big data industry AFinancial investmentA1The proportion of financial investment in the total financial amountThe proportion of big data industry investment in the total financial investmentThe total number of invested personnelThe number of the personnel engaging in big data industryThe structure of invested personnelThe situation of the education background and professional titles of the personnel engaging in big data industryInvested material resourcesThe amount, category, and so on of material resources invested in big data industryFinancial implementation A2The completion rate of financial investment fund planThe ratio of the actual amount of funds invested to the amount of funds plannedFinancial capital management systemWhether the use of management system of financial investment funds is perfectCompletion status of the technical indexesWhether the technical indexes meet the expected goalsProfit-tax rate of financial investmentThe ratio of newly increased taxes to total financial investmentFinancial profitA3Output rate of financial investmentThe ratio of financial investment to the output of big data industryApplication conversion rate of big data achievementsThe ratio of the achievements of the transformed application to the successful application achievementsPer-capita number of published articles of the invested personnelThe ratio of the number of published articles to the total number of invested personnelPer-capita number of obtained patents of the invested personnelThe ratio of the number of obtained patents to the total number of invested personnel


## 3.1. Evaluation Index

The diversity of big data output performance determines the multiobjective feature of its evaluation, so the financial support performance evaluation system of the big data industry should be a multiobjective evaluation system. The evaluation system can be constructed according to the idea of an analytic hierarchy process, reasonably choosing and setting the index system by hierarchy, attribute, quantity, and quality, and using the performance evaluation as the core. The evaluation index system is divided into the target hierarchy (financial investment performance), the criterion hierarchy (describing main aspects of the performance), and the index hierarchy (that can reflect the quantitative and qualitative indexes of the elements’ characteristics).According to the characteristics of financial support in the big data industry, this paper chooses three major categories of indexes, namely, financial investment, financial implementation, and financial profit, to reflect the financial support performance of the big data industry.The financial investment mainly reflects the government’s degree of emphasis on the investment in the big data industry. This paper selects four indexes to describe it from the perspective of investment, and the four indexes are the proportion of financial investments in the total financial amount, the total number of invested personnel, the structure of invested personnel, and the invested material resources.The financial implementation mainly reflects the execution status in actual implementation of the government’s financial support for the big data industry. There are four secondary indexes, and they are, respectively, the completion rate of a financial investment fund plan, the financial capital management system, the completion status of the technical indexes, and the profit-tax rate of financial investment.The financial profit mainly reflects the economic and social benefits brought by the development of the financially supported big data industry. This paper chooses four secondary indexes to measure it, and they are the output rate of financial investment; the application conversion rate of big data achievements; the per-capita number of published articles of the invested personnel; and the per-capita number of obtained patents of the invested personnel.


## 3.2. Construction of Fuzzy Hierarchy Model

The fuzzy hierarchy model is constructed according to the evaluation index system, in which the criterion hierarchy of financial support performance evaluation index system of the big data industry has three elements, and the index hierarchy has 12 indexes. The evaluation content and scope of each level are shown in Table1:Table 1
Financial support performance evaluation index system of big data industry.
Target hierarchyCriterion hierarchyIndex hierarchyConcrete meaningFinancial support performance index evaluation system of the big data industry AFinancial investmentA1The proportion of financial investment in the total financial amountThe proportion of big data industry investment in the total financial investmentThe total number of invested personnelThe number of the personnel engaging in big data industryThe structure of invested personnelThe situation of the education background and professional titles of the personnel engaging in big data industryInvested material resourcesThe amount, category, and so on of material resources invested in big data industryFinancial implementation A2The completion rate of financial investment fund planThe ratio of the actual amount of funds invested to the amount of funds plannedFinancial capital management systemWhether the use of management system of financial investment funds is perfectCompletion status of the technical indexesWhether the technical indexes meet the expected goalsProfit-tax rate of financial investmentThe ratio of newly increased taxes to total financial investmentFinancial profitA3Output rate of financial investmentThe ratio of financial investment to the output of big data industryApplication conversion rate of big data achievementsThe ratio of the achievements of the transformed application to the successful application achievementsPer-capita number of published articles of the invested personnelThe ratio of the number of published articles to the total number of invested personnelPer-capita number of obtained patents of the invested personnelThe ratio of the number of obtained patents to the total number of invested personnel


## 4. Empirical Analysis

### 4.1. Determination of Index Weight

#### 4.1.1. Construction of Judgment Matrix

Before calculating the weight of each index, the relative importance of each index should be judged, and these judgments are expressed numerically to generate the judgment matrix, as shown in Table2. The judgment matrix represents the comparison of the relative importance between the relevant indexes of this hierarchy aiming at the target of the previous hierarchy. Based on the idea of analytic hierarchy process and on the basis of the above hierarchical structure of financial support evaluation index system, let us suppose that the judgment matrix to be constructed is A=aij, in which the value of aij is the relative importance of the indexes and is determined by the following method.Table 2
Judgment matrix scale table.
ScaleXijMeaning1Factori is as important as factor j3Factori is slightly more important than factor j5Factori is more important than factor j7Factori is very important compared with factor j9Factori is absolutely important compared with factor j2, 4, 6, 8Med-value of the above two adjacent judgmentsReciprocal 1,1/2,… 1/9The judgment value obtained by the comparison of factorj and factor i is the reciprocal of XijXji=1/aijXii=1.In this paper, the relative importance of the indexes is obtained through pairwise comparison according to the specific situation, and then the judgment matrices of the primary and secondary indexes are obtained, as shown in Table3.Table 3
Primary indexes judgment matrix A.
Financial support performanceAFinancial investmentA1Financial implementationA2Financial profitA3Financial investmentA111/31/7Financial implementationA2311/2Financial profitA3721Judgment matrix of the first-order indexes is as follows:(1)A=113173112721.Similarly, judgment matrix of the second-order indexes can be obtained as shown in the following formula:(2)A1=13311311131311131331A2=1133531561315131516131A3=313134134114131114131.


#### 4.1.2. Normalization Processing

The methods of calculating weight include the weighted least square method, the eigenvalue method, the square root method, the sum-product method, and so on. In the calculation of the financial support performance index evaluation system of the big data industry discussed in this article, the sum-product method is adopted to calculate the weight for the judgment matrix: normalize each column of the judgment matrix first, then sum the matrix after the normalization of columns according to rows, and normalize the vectors after the summation as the weight of each index factor. Take the judgment matrix A, for example,(3)A=113173112721⟶normalization0.090.100.090.270.300.300.640.600.61⟶summation according to rows0.280.871.85⟶normalization0.090.290.62.Similarly, the weight vectors of each element of each hierarchy of the judgment matrices ofA1, A2, and A3 can be calculated.Thus, we can obtain the weights of the index in Table1, and the results are shown in Table 4.Table 4
Weights of the indexes.
Target hierarchyCriterion hierarchyWeightIndex hierarchyWeightFinancial support performance index evaluation system of the big data industry AFinancial investmentA10.09The proportion of financial investment in the total financial amount0.35The total number of invested personnel0.15The structure of invested personnel0.15Invested material resources0.35Financial implementationA20.29The completion rate of financial investment fund plan0.27Financial capital management system0.55Completion status of the technical indexes0.12Profit-tax rate of financial investment0.06Financial profitA30.62Output rate of financial investment0.27Application conversion rate of big data achievements0.52Per-capita number of published articles of the invested personnel0.10Per-capita number of obtained patents of the invested personnel0.11


#### 4.1.3. Matrix Consistency Check

The pairwise comparison matrix constructed above constitutes the foundation of decision analysis, and when comparing and judging the relative importance of the indexes because it is a man-made comparison and judgment, there are often inconsistent judgments. Therefore, the consistency must be checked to find out whether the weight vector values calculated are scientific and reasonable. The specific checking steps are as follows.Firstly, calculate the eigenvalue of the maximum of the judgment matrixλmax: λmax=1/n∑i=1n∑j=1naijwj/wj.Calculate by taking the judgment matrix A as an example,w=0.09,0.29,0.62T.(4)Aw=0.28,0.87,1.83TSo, the judgment matrixA’s λmax  =  1/30.28/0.09+0.87/0.29+1.83/0.62  = 3.02.Secondly, calculate the deviation index from the consistency, the CI valueCI=λmax−n/n−1, and n is the number of indexes in the matrix. The CI value of A is calculated according to the above formula: CI =  3.02−3/3−1  = 0.01.Thirdly, find the average random consistency index, the RI value, as shown in Table5.Table 5
RI value.
n2345678910RI00.51490.89311.11851.24941.34501.42001.46161.4874Fourthly, calculate the random consistency ratio CR,CR=CI/RI. If CR≥0.1, the judgment matrix constructed fails in the consistency check and it needs to be adjusted; and if CR<0.1, it can be considered that the judgment matrix has satisfactory consistency and the index weights obtained by the judgment matrix can be applied to the comprehensive measurement model. As CR is obtained by the above calculation: CR = CI/RI = 0.01/0.5149 = 0.019 < 0.1, which indicates that A has passed the consistency check.Similarly, through processing and checking the judgment matrix of the index hierarchy, it is found that the judgment matrices ofA1, A2, and A3 pass the consistency check.


### 4.2. Fuzzy Comprehensive Evaluation

The fuzzy mathematics comprehensive judgment is a method that makes comprehensive judgment of the subordinate hierarchy status of the evaluation object from multiple factors (or indexes) based on the composition principle of fuzzy relations. The following is a comprehensive judgment of fuzzy mathematics based on the above analytic hierarchy process.


#### 4.2.1. Determine the Set of Evaluation Factors

The financial support for the big data industry is a complicated process, and the factors that constitute it are complex, changeable, and uncertain, so these factors constitute a fuzzy set. How to select the main factors reflecting the financial support performance from this fuzzy set is the key in establishing the comprehensive evaluation model of financial support performance for the big data industry. According to the setting of the index system of the above analytic hierarchy process, the financial support performance evaluation system of the big data industry contains 12 secondary evaluation indexes. Thus, the fuzzy factor vector of the financial support performance evaluation system of the big data industry is obtained:(5)U=u1,u2,u3,…u12.


#### 4.2.2. Establish a Set of Judgment Vectors

Since there are three main factors ofA1, A2, and A3 and 12 secondary factors that affect the “financial support performance,” the weights of the factors can be given according to the hierarchical analysis, as shown in Table 4. The judgment set consisting of five elements can be given for each of the secondary factors, for example (very good, good, general, poor, and very poor), (very high, high, general, low, and very low), and (a great many, many, general, a few, and few), as shown in Table 6.Table 6
Judgment set of the factors.
FactorLevel 1Level 2Level 3Level 4Level 5The proportion of financial investment in the total financial amountVery highHighGeneralLowVery lowThe total number of invested personnelA great manyManyGeneralA fewFewThe structure of invested personnelVery goodGoodGeneralPoorVery poorInvested material resourcesA great manyManyGeneralA fewFewThe completion rate of financial investment fund planVery goodGoodGeneralPoorVery poorFinancial capital management systemVery goodGoodGeneralPoorVery poorCompletion status of the technical indexesVery goodGoodGeneralPoorVery poorProfit-tax rate of financial investmentVery highHighGeneralLowVery lowOutput rate of financial investmentVery highHighGeneralLowVery lowApplication conversion rate of big data achievementsVery highHighGeneralLowVery lowPer-capita number of published articles of the invested personnelA great manyManyGeneralA fewFewPer-capita number of obtained patents of the invested personnelA great manyManyGeneralA fewFew


#### 4.2.3. Acquire Fuzzy Evaluation Information

Although using the method designated by experts is of a certain significance, its scientificity is questionable. Studies show that people can give more accurate judgment only at pairwise comparison, and when the number of factors is greater than 4, the fuzziness of determining the weight is more obvious, and the processing by the method of constructing a fuzzy consistent judgment matrix can eliminate the influence of man-made factors.The fuzzy consistent judgment matrixR represents the comparison of the relative importance between a certain factor in the previous hierarchy and its related factors in this hierarchy. Suppose the factor C in the previous hierarchy is related to the factors a1, a2, a3......an in the next hierarchy, then the fuzzy consistent judgment matrix F can be expressed as(6)F=r11r21⋯r1nr21r22⋯r2n⋯⋯⋯⋯rn1rn1⋯rnn.Thereinto,rij indicates that factor ai has a subordinate degree of the fuzzy relation of “ai is more important than aj” with factor aj. It can be described in Table 7.Table 7
0.1–0.9 scale table.
DefinitionScaleMeaningEqually important0.5Two factors are equally important by comparisonSlightly more important0.6Factor A is slightly more important than factor B by comparisonObviously more important0.7Factor A is obviously more important than factor B by comparisonMuch more important0.8Factor A is much more important than factor B by comparisonExtremely more important0.9Factor A is extremely more important than factor B by comparisonReverse comparison0.1–0.4aij=1−ajiExperts are asked to give a fuzzy judgment of the pairwise comparison of the above 12 factors, and the following fuzzy complementary judgment matrix is obtained after the integration and adjustment:(7)A1=rij=0.50.60.80.60.40.50.70.50.20.30.50.30.40.50.70.50.40.50.70.5A2=0.60.60.80.60.50.50.70.50.30.30.50.30.50.50.70.50.50.50.70.5A3=0.70.60.60.40.60.50.50.30.40.30.30.10.60.50.50.30.60.50.50.3.


#### 4.2.4. Determine the Fuzzy Weight Vector

The weight of each factor in the single-factor set is determined by analytic hierarchy process, and thus the fuzzy weight vector is obtainedΩ=w1w2w3w4. The weight vector is obtained according to Table 4.(8)Ω1=0.350.150.150.35TΩ2=0.270.550.120.06TΩ3=0.270.520.100.11T.


#### 4.2.5. Comprehensive Judgment

The comprehensive judgment models adopted by the fuzzy relation matrix F with respect to weightΩ, include the main factor determining model, the main factor highlighting model, the weighted average model, and so on. The weighted average model M•,+ can be used if all factors are considered to have an impact on the financial support performance.(9)A1Ω1=0.3420.2100.4040.2100.210A2Ω2=0.4070.2100.1330.2100.210A2Ω2=0.3640.2100.1260.2100.210.


#### 4.2.6. Assign Values to the Judgment Set

The components of the comprehensive evaluation level vector obtained above are generally a group of decimals, and a group of percentages can be obtained through multiplying 100%, respectively, by each of the elements of the vector, the meaning of which can be considered to be the percentage of people favoring the evaluation levels of “Very satisfied, Satisfied, Basically satisfied, Dissatisfied, and Very dissatisfied.” In practical applications, each level of the evaluation set is usually represented by a corresponding specific number. For this reason, we use the median method to assign values to the evaluation set and determine the centesimal system scores from high to low for each evaluation level.v1 means the score interval of “Very satisfied” is [90, 100]; v2 means the score interval of “Satisfied” is [80, 89]; v3 means the score interval of “Basically satisfied” is [60, 79]; v4 means the score interval of “Dissatisfied” is [50, 59]; v5 means the score interval of “Very dissatisfied” is [30, 49]. Let us use the medians of the corresponding score sections of 95, 85, 70, 55, and 40 to assign values to the evaluation levels and assume that the valuation vectors of the judgment set, V=v1v2v3v4v5, are (95, 80, 70, 55, and 40), so the comprehensive evaluation valuation of A1 is 95,80,70,55,40, 0.342,0.210,0.404,0.210,0.210T  = 97.485. Similarly, we can obtain the comprehensive evaluation valuations of A2 and A3, and they are 84.702 and 80.091, respectively.


## 4.1. Determination of Index Weight

### 4.1.1. Construction of Judgment Matrix

Before calculating the weight of each index, the relative importance of each index should be judged, and these judgments are expressed numerically to generate the judgment matrix, as shown in Table2. The judgment matrix represents the comparison of the relative importance between the relevant indexes of this hierarchy aiming at the target of the previous hierarchy. Based on the idea of analytic hierarchy process and on the basis of the above hierarchical structure of financial support evaluation index system, let us suppose that the judgment matrix to be constructed is A=aij, in which the value of aij is the relative importance of the indexes and is determined by the following method.Table 2
Judgment matrix scale table.
ScaleXijMeaning1Factori is as important as factor j3Factori is slightly more important than factor j5Factori is more important than factor j7Factori is very important compared with factor j9Factori is absolutely important compared with factor j2, 4, 6, 8Med-value of the above two adjacent judgmentsReciprocal 1,1/2,… 1/9The judgment value obtained by the comparison of factorj and factor i is the reciprocal of XijXji=1/aijXii=1.In this paper, the relative importance of the indexes is obtained through pairwise comparison according to the specific situation, and then the judgment matrices of the primary and secondary indexes are obtained, as shown in Table3.Table 3
Primary indexes judgment matrix A.
Financial support performanceAFinancial investmentA1Financial implementationA2Financial profitA3Financial investmentA111/31/7Financial implementationA2311/2Financial profitA3721Judgment matrix of the first-order indexes is as follows:(1)A=113173112721.Similarly, judgment matrix of the second-order indexes can be obtained as shown in the following formula:(2)A1=13311311131311131331A2=1133531561315131516131A3=313134134114131114131.


### 4.1.2. Normalization Processing

The methods of calculating weight include the weighted least square method, the eigenvalue method, the square root method, the sum-product method, and so on. In the calculation of the financial support performance index evaluation system of the big data industry discussed in this article, the sum-product method is adopted to calculate the weight for the judgment matrix: normalize each column of the judgment matrix first, then sum the matrix after the normalization of columns according to rows, and normalize the vectors after the summation as the weight of each index factor. Take the judgment matrix A, for example,(3)A=113173112721⟶normalization0.090.100.090.270.300.300.640.600.61⟶summation according to rows0.280.871.85⟶normalization0.090.290.62.Similarly, the weight vectors of each element of each hierarchy of the judgment matrices ofA1, A2, and A3 can be calculated.Thus, we can obtain the weights of the index in Table1, and the results are shown in Table 4.Table 4
Weights of the indexes.
Target hierarchyCriterion hierarchyWeightIndex hierarchyWeightFinancial support performance index evaluation system of the big data industry AFinancial investmentA10.09The proportion of financial investment in the total financial amount0.35The total number of invested personnel0.15The structure of invested personnel0.15Invested material resources0.35Financial implementationA20.29The completion rate of financial investment fund plan0.27Financial capital management system0.55Completion status of the technical indexes0.12Profit-tax rate of financial investment0.06Financial profitA30.62Output rate of financial investment0.27Application conversion rate of big data achievements0.52Per-capita number of published articles of the invested personnel0.10Per-capita number of obtained patents of the invested personnel0.11


### 4.1.3. Matrix Consistency Check

The pairwise comparison matrix constructed above constitutes the foundation of decision analysis, and when comparing and judging the relative importance of the indexes because it is a man-made comparison and judgment, there are often inconsistent judgments. Therefore, the consistency must be checked to find out whether the weight vector values calculated are scientific and reasonable. The specific checking steps are as follows.Firstly, calculate the eigenvalue of the maximum of the judgment matrixλmax: λmax=1/n∑i=1n∑j=1naijwj/wj.Calculate by taking the judgment matrix A as an example,w=0.09,0.29,0.62T.(4)Aw=0.28,0.87,1.83TSo, the judgment matrixA’s λmax  =  1/30.28/0.09+0.87/0.29+1.83/0.62  = 3.02.Secondly, calculate the deviation index from the consistency, the CI valueCI=λmax−n/n−1, and n is the number of indexes in the matrix. The CI value of A is calculated according to the above formula: CI =  3.02−3/3−1  = 0.01.Thirdly, find the average random consistency index, the RI value, as shown in Table5.Table 5
RI value.
n2345678910RI00.51490.89311.11851.24941.34501.42001.46161.4874Fourthly, calculate the random consistency ratio CR,CR=CI/RI. If CR≥0.1, the judgment matrix constructed fails in the consistency check and it needs to be adjusted; and if CR<0.1, it can be considered that the judgment matrix has satisfactory consistency and the index weights obtained by the judgment matrix can be applied to the comprehensive measurement model. As CR is obtained by the above calculation: CR = CI/RI = 0.01/0.5149 = 0.019 < 0.1, which indicates that A has passed the consistency check.Similarly, through processing and checking the judgment matrix of the index hierarchy, it is found that the judgment matrices ofA1, A2, and A3 pass the consistency check.


## 4.1.1. Construction of Judgment Matrix

Before calculating the weight of each index, the relative importance of each index should be judged, and these judgments are expressed numerically to generate the judgment matrix, as shown in Table2. The judgment matrix represents the comparison of the relative importance between the relevant indexes of this hierarchy aiming at the target of the previous hierarchy. Based on the idea of analytic hierarchy process and on the basis of the above hierarchical structure of financial support evaluation index system, let us suppose that the judgment matrix to be constructed is A=aij, in which the value of aij is the relative importance of the indexes and is determined by the following method.Table 2
Judgment matrix scale table.
ScaleXijMeaning1Factori is as important as factor j3Factori is slightly more important than factor j5Factori is more important than factor j7Factori is very important compared with factor j9Factori is absolutely important compared with factor j2, 4, 6, 8Med-value of the above two adjacent judgmentsReciprocal 1,1/2,… 1/9The judgment value obtained by the comparison of factorj and factor i is the reciprocal of XijXji=1/aijXii=1.In this paper, the relative importance of the indexes is obtained through pairwise comparison according to the specific situation, and then the judgment matrices of the primary and secondary indexes are obtained, as shown in Table3.Table 3
Primary indexes judgment matrix A.
Financial support performanceAFinancial investmentA1Financial implementationA2Financial profitA3Financial investmentA111/31/7Financial implementationA2311/2Financial profitA3721Judgment matrix of the first-order indexes is as follows:(1)A=113173112721.Similarly, judgment matrix of the second-order indexes can be obtained as shown in the following formula:(2)A1=13311311131311131331A2=1133531561315131516131A3=313134134114131114131.


## 4.1.2. Normalization Processing

The methods of calculating weight include the weighted least square method, the eigenvalue method, the square root method, the sum-product method, and so on. In the calculation of the financial support performance index evaluation system of the big data industry discussed in this article, the sum-product method is adopted to calculate the weight for the judgment matrix: normalize each column of the judgment matrix first, then sum the matrix after the normalization of columns according to rows, and normalize the vectors after the summation as the weight of each index factor. Take the judgment matrix A, for example,(3)A=113173112721⟶normalization0.090.100.090.270.300.300.640.600.61⟶summation according to rows0.280.871.85⟶normalization0.090.290.62.Similarly, the weight vectors of each element of each hierarchy of the judgment matrices ofA1, A2, and A3 can be calculated.Thus, we can obtain the weights of the index in Table1, and the results are shown in Table 4.Table 4
Weights of the indexes.
Target hierarchyCriterion hierarchyWeightIndex hierarchyWeightFinancial support performance index evaluation system of the big data industry AFinancial investmentA10.09The proportion of financial investment in the total financial amount0.35The total number of invested personnel0.15The structure of invested personnel0.15Invested material resources0.35Financial implementationA20.29The completion rate of financial investment fund plan0.27Financial capital management system0.55Completion status of the technical indexes0.12Profit-tax rate of financial investment0.06Financial profitA30.62Output rate of financial investment0.27Application conversion rate of big data achievements0.52Per-capita number of published articles of the invested personnel0.10Per-capita number of obtained patents of the invested personnel0.11


## 4.1.3. Matrix Consistency Check

The pairwise comparison matrix constructed above constitutes the foundation of decision analysis, and when comparing and judging the relative importance of the indexes because it is a man-made comparison and judgment, there are often inconsistent judgments. Therefore, the consistency must be checked to find out whether the weight vector values calculated are scientific and reasonable. The specific checking steps are as follows.Firstly, calculate the eigenvalue of the maximum of the judgment matrixλmax: λmax=1/n∑i=1n∑j=1naijwj/wj.Calculate by taking the judgment matrix A as an example,w=0.09,0.29,0.62T.(4)Aw=0.28,0.87,1.83TSo, the judgment matrixA’s λmax  =  1/30.28/0.09+0.87/0.29+1.83/0.62  = 3.02.Secondly, calculate the deviation index from the consistency, the CI valueCI=λmax−n/n−1, and n is the number of indexes in the matrix. The CI value of A is calculated according to the above formula: CI =  3.02−3/3−1  = 0.01.Thirdly, find the average random consistency index, the RI value, as shown in Table5.Table 5
RI value.
n2345678910RI00.51490.89311.11851.24941.34501.42001.46161.4874Fourthly, calculate the random consistency ratio CR,CR=CI/RI. If CR≥0.1, the judgment matrix constructed fails in the consistency check and it needs to be adjusted; and if CR<0.1, it can be considered that the judgment matrix has satisfactory consistency and the index weights obtained by the judgment matrix can be applied to the comprehensive measurement model. As CR is obtained by the above calculation: CR = CI/RI = 0.01/0.5149 = 0.019 < 0.1, which indicates that A has passed the consistency check.Similarly, through processing and checking the judgment matrix of the index hierarchy, it is found that the judgment matrices ofA1, A2, and A3 pass the consistency check.


## 4.2. Fuzzy Comprehensive Evaluation

The fuzzy mathematics comprehensive judgment is a method that makes comprehensive judgment of the subordinate hierarchy status of the evaluation object from multiple factors (or indexes) based on the composition principle of fuzzy relations. The following is a comprehensive judgment of fuzzy mathematics based on the above analytic hierarchy process.


### 4.2.1. Determine the Set of Evaluation Factors

The financial support for the big data industry is a complicated process, and the factors that constitute it are complex, changeable, and uncertain, so these factors constitute a fuzzy set. How to select the main factors reflecting the financial support performance from this fuzzy set is the key in establishing the comprehensive evaluation model of financial support performance for the big data industry. According to the setting of the index system of the above analytic hierarchy process, the financial support performance evaluation system of the big data industry contains 12 secondary evaluation indexes. Thus, the fuzzy factor vector of the financial support performance evaluation system of the big data industry is obtained:(5)U=u1,u2,u3,…u12.


### 4.2.2. Establish a Set of Judgment Vectors

Since there are three main factors ofA1, A2, and A3 and 12 secondary factors that affect the “financial support performance,” the weights of the factors can be given according to the hierarchical analysis, as shown in Table 4. The judgment set consisting of five elements can be given for each of the secondary factors, for example (very good, good, general, poor, and very poor), (very high, high, general, low, and very low), and (a great many, many, general, a few, and few), as shown in Table 6.Table 6
Judgment set of the factors.
FactorLevel 1Level 2Level 3Level 4Level 5The proportion of financial investment in the total financial amountVery highHighGeneralLowVery lowThe total number of invested personnelA great manyManyGeneralA fewFewThe structure of invested personnelVery goodGoodGeneralPoorVery poorInvested material resourcesA great manyManyGeneralA fewFewThe completion rate of financial investment fund planVery goodGoodGeneralPoorVery poorFinancial capital management systemVery goodGoodGeneralPoorVery poorCompletion status of the technical indexesVery goodGoodGeneralPoorVery poorProfit-tax rate of financial investmentVery highHighGeneralLowVery lowOutput rate of financial investmentVery highHighGeneralLowVery lowApplication conversion rate of big data achievementsVery highHighGeneralLowVery lowPer-capita number of published articles of the invested personnelA great manyManyGeneralA fewFewPer-capita number of obtained patents of the invested personnelA great manyManyGeneralA fewFew


### 4.2.3. Acquire Fuzzy Evaluation Information

Although using the method designated by experts is of a certain significance, its scientificity is questionable. Studies show that people can give more accurate judgment only at pairwise comparison, and when the number of factors is greater than 4, the fuzziness of determining the weight is more obvious, and the processing by the method of constructing a fuzzy consistent judgment matrix can eliminate the influence of man-made factors.The fuzzy consistent judgment matrixR represents the comparison of the relative importance between a certain factor in the previous hierarchy and its related factors in this hierarchy. Suppose the factor C in the previous hierarchy is related to the factors a1, a2, a3......an in the next hierarchy, then the fuzzy consistent judgment matrix F can be expressed as(6)F=r11r21⋯r1nr21r22⋯r2n⋯⋯⋯⋯rn1rn1⋯rnn.Thereinto,rij indicates that factor ai has a subordinate degree of the fuzzy relation of “ai is more important than aj” with factor aj. It can be described in Table 7.Table 7
0.1–0.9 scale table.
DefinitionScaleMeaningEqually important0.5Two factors are equally important by comparisonSlightly more important0.6Factor A is slightly more important than factor B by comparisonObviously more important0.7Factor A is obviously more important than factor B by comparisonMuch more important0.8Factor A is much more important than factor B by comparisonExtremely more important0.9Factor A is extremely more important than factor B by comparisonReverse comparison0.1–0.4aij=1−ajiExperts are asked to give a fuzzy judgment of the pairwise comparison of the above 12 factors, and the following fuzzy complementary judgment matrix is obtained after the integration and adjustment:(7)A1=rij=0.50.60.80.60.40.50.70.50.20.30.50.30.40.50.70.50.40.50.70.5A2=0.60.60.80.60.50.50.70.50.30.30.50.30.50.50.70.50.50.50.70.5A3=0.70.60.60.40.60.50.50.30.40.30.30.10.60.50.50.30.60.50.50.3.


### 4.2.4. Determine the Fuzzy Weight Vector

The weight of each factor in the single-factor set is determined by analytic hierarchy process, and thus the fuzzy weight vector is obtainedΩ=w1w2w3w4. The weight vector is obtained according to Table 4.(8)Ω1=0.350.150.150.35TΩ2=0.270.550.120.06TΩ3=0.270.520.100.11T.


### 4.2.5. Comprehensive Judgment

The comprehensive judgment models adopted by the fuzzy relation matrix F with respect to weightΩ, include the main factor determining model, the main factor highlighting model, the weighted average model, and so on. The weighted average model M•,+ can be used if all factors are considered to have an impact on the financial support performance.(9)A1Ω1=0.3420.2100.4040.2100.210A2Ω2=0.4070.2100.1330.2100.210A2Ω2=0.3640.2100.1260.2100.210.


### 4.2.6. Assign Values to the Judgment Set

The components of the comprehensive evaluation level vector obtained above are generally a group of decimals, and a group of percentages can be obtained through multiplying 100%, respectively, by each of the elements of the vector, the meaning of which can be considered to be the percentage of people favoring the evaluation levels of “Very satisfied, Satisfied, Basically satisfied, Dissatisfied, and Very dissatisfied.” In practical applications, each level of the evaluation set is usually represented by a corresponding specific number. For this reason, we use the median method to assign values to the evaluation set and determine the centesimal system scores from high to low for each evaluation level.v1 means the score interval of “Very satisfied” is [90, 100]; v2 means the score interval of “Satisfied” is [80, 89]; v3 means the score interval of “Basically satisfied” is [60, 79]; v4 means the score interval of “Dissatisfied” is [50, 59]; v5 means the score interval of “Very dissatisfied” is [30, 49]. Let us use the medians of the corresponding score sections of 95, 85, 70, 55, and 40 to assign values to the evaluation levels and assume that the valuation vectors of the judgment set, V=v1v2v3v4v5, are (95, 80, 70, 55, and 40), so the comprehensive evaluation valuation of A1 is 95,80,70,55,40, 0.342,0.210,0.404,0.210,0.210T  = 97.485. Similarly, we can obtain the comprehensive evaluation valuations of A2 and A3, and they are 84.702 and 80.091, respectively.


## 4.2.1. Determine the Set of Evaluation Factors

The financial support for the big data industry is a complicated process, and the factors that constitute it are complex, changeable, and uncertain, so these factors constitute a fuzzy set. How to select the main factors reflecting the financial support performance from this fuzzy set is the key in establishing the comprehensive evaluation model of financial support performance for the big data industry. According to the setting of the index system of the above analytic hierarchy process, the financial support performance evaluation system of the big data industry contains 12 secondary evaluation indexes. Thus, the fuzzy factor vector of the financial support performance evaluation system of the big data industry is obtained:(5)U=u1,u2,u3,…u12.


## 4.2.2. Establish a Set of Judgment Vectors

Since there are three main factors ofA1, A2, and A3 and 12 secondary factors that affect the “financial support performance,” the weights of the factors can be given according to the hierarchical analysis, as shown in Table 4. The judgment set consisting of five elements can be given for each of the secondary factors, for example (very good, good, general, poor, and very poor), (very high, high, general, low, and very low), and (a great many, many, general, a few, and few), as shown in Table 6.Table 6
Judgment set of the factors.
FactorLevel 1Level 2Level 3Level 4Level 5The proportion of financial investment in the total financial amountVery highHighGeneralLowVery lowThe total number of invested personnelA great manyManyGeneralA fewFewThe structure of invested personnelVery goodGoodGeneralPoorVery poorInvested material resourcesA great manyManyGeneralA fewFewThe completion rate of financial investment fund planVery goodGoodGeneralPoorVery poorFinancial capital management systemVery goodGoodGeneralPoorVery poorCompletion status of the technical indexesVery goodGoodGeneralPoorVery poorProfit-tax rate of financial investmentVery highHighGeneralLowVery lowOutput rate of financial investmentVery highHighGeneralLowVery lowApplication conversion rate of big data achievementsVery highHighGeneralLowVery lowPer-capita number of published articles of the invested personnelA great manyManyGeneralA fewFewPer-capita number of obtained patents of the invested personnelA great manyManyGeneralA fewFew


## 4.2.3. Acquire Fuzzy Evaluation Information

Although using the method designated by experts is of a certain significance, its scientificity is questionable. Studies show that people can give more accurate judgment only at pairwise comparison, and when the number of factors is greater than 4, the fuzziness of determining the weight is more obvious, and the processing by the method of constructing a fuzzy consistent judgment matrix can eliminate the influence of man-made factors.The fuzzy consistent judgment matrixR represents the comparison of the relative importance between a certain factor in the previous hierarchy and its related factors in this hierarchy. Suppose the factor C in the previous hierarchy is related to the factors a1, a2, a3......an in the next hierarchy, then the fuzzy consistent judgment matrix F can be expressed as(6)F=r11r21⋯r1nr21r22⋯r2n⋯⋯⋯⋯rn1rn1⋯rnn.Thereinto,rij indicates that factor ai has a subordinate degree of the fuzzy relation of “ai is more important than aj” with factor aj. It can be described in Table 7.Table 7
0.1–0.9 scale table.
DefinitionScaleMeaningEqually important0.5Two factors are equally important by comparisonSlightly more important0.6Factor A is slightly more important than factor B by comparisonObviously more important0.7Factor A is obviously more important than factor B by comparisonMuch more important0.8Factor A is much more important than factor B by comparisonExtremely more important0.9Factor A is extremely more important than factor B by comparisonReverse comparison0.1–0.4aij=1−ajiExperts are asked to give a fuzzy judgment of the pairwise comparison of the above 12 factors, and the following fuzzy complementary judgment matrix is obtained after the integration and adjustment:(7)A1=rij=0.50.60.80.60.40.50.70.50.20.30.50.30.40.50.70.50.40.50.70.5A2=0.60.60.80.60.50.50.70.50.30.30.50.30.50.50.70.50.50.50.70.5A3=0.70.60.60.40.60.50.50.30.40.30.30.10.60.50.50.30.60.50.50.3.


## 4.2.4. Determine the Fuzzy Weight Vector

The weight of each factor in the single-factor set is determined by analytic hierarchy process, and thus the fuzzy weight vector is obtainedΩ=w1w2w3w4. The weight vector is obtained according to Table 4.(8)Ω1=0.350.150.150.35TΩ2=0.270.550.120.06TΩ3=0.270.520.100.11T.


## 4.2.5. Comprehensive Judgment

The comprehensive judgment models adopted by the fuzzy relation matrix F with respect to weightΩ, include the main factor determining model, the main factor highlighting model, the weighted average model, and so on. The weighted average model M•,+ can be used if all factors are considered to have an impact on the financial support performance.(9)A1Ω1=0.3420.2100.4040.2100.210A2Ω2=0.4070.2100.1330.2100.210A2Ω2=0.3640.2100.1260.2100.210.


## 4.2.6. Assign Values to the Judgment Set

The components of the comprehensive evaluation level vector obtained above are generally a group of decimals, and a group of percentages can be obtained through multiplying 100%, respectively, by each of the elements of the vector, the meaning of which can be considered to be the percentage of people favoring the evaluation levels of “Very satisfied, Satisfied, Basically satisfied, Dissatisfied, and Very dissatisfied.” In practical applications, each level of the evaluation set is usually represented by a corresponding specific number. For this reason, we use the median method to assign values to the evaluation set and determine the centesimal system scores from high to low for each evaluation level.v1 means the score interval of “Very satisfied” is [90, 100]; v2 means the score interval of “Satisfied” is [80, 89]; v3 means the score interval of “Basically satisfied” is [60, 79]; v4 means the score interval of “Dissatisfied” is [50, 59]; v5 means the score interval of “Very dissatisfied” is [30, 49]. Let us use the medians of the corresponding score sections of 95, 85, 70, 55, and 40 to assign values to the evaluation levels and assume that the valuation vectors of the judgment set, V=v1v2v3v4v5, are (95, 80, 70, 55, and 40), so the comprehensive evaluation valuation of A1 is 95,80,70,55,40, 0.342,0.210,0.404,0.210,0.210T  = 97.485. Similarly, we can obtain the comprehensive evaluation valuations of A2 and A3, and they are 84.702 and 80.091, respectively.


## 5. Analysis of Results

According to the characteristics of the financial support performance evaluation of the big data industry, we evaluate from three aspects of financial investment, financial implementation, and financial profit. By consulting relevant experts and scholars and according to the analytic hierarchy process, the respective weights of the three evaluation subjects are obtained, and thus the weight matrix is known asw=0.09,0.29,0.62T. Suppose the centesimal system scores of the comprehensive evaluation results of the three aspects are H1,H2,andH3, respectively so, the final evaluation of the financial support performance of the big data industry is as follows:(10)Htotalscore=wH1H2H3=0.09∗H1+0.29∗H2+0.62∗H3.It can be known from the evaluation set valuation content thatH1=97.485, H2=84.702, and H3=80.091 and combining with the above formula, the final score of the financial support performance evaluation of the big data industry is obtained.(11)Htotalscore=0.09∗97.485+0.29∗84.702+0.62∗80.091=83.001.So, the final score of the financial support performance evaluation of the big data industry is 83.001.Among the three primary evaluation indexes selected in this paper, it can be seen from the weight vectors that the weight or proportion of financial profit is the largest, which shows that improving the economic and social benefits of the big data industry is the most important way to financially support the development of big data industry. Therefore, it is necessary to strengthen the social contribution performance of enterprises, enhance the sense of social responsibility of enterprises, endeavor to improve the research and innovation ability of enterprises, and create the core competitiveness of enterprises at the same time.


## 6. Conclusion and Recommendation

This paper aims to evaluate the financial support of the big data industry in China. According to the evaluation process, by identifying the factors affecting the performance evaluation of financial support, choosing different evaluation methods, establishing a suitable evaluation, and in conjunction with the results of the evaluation of 3, a detailed analysis of financial support performance for large business data is carried out. Based on the understanding of each influencing factor, highlight the main and secondary effects. Pay special attention to the formulation and implementation of some financial rules, and provide better development suggestions and support for the big data industry.In terms of the financial investment, the most important thing is the proportion of financial investment in the total financial amount and the invested material resources, which shows that the healthy and rapid development of the big data industry needs heavy financial investment from our country, including the investment of material resources, such as capital, research and development personnel, and infrastructure. This requires the country to integrate existing policy measures, perfect the financial policy system of the big data industry, and clarify the key areas and scope of the financial support.In terms of the financial implementation, the weight of the financial fund management system is the largest, which indicates that fund management plays an important role in the development of the big data industry. In order to guarantee the reasonable and sound development of the big data industry, it is necessary for the country to introduce the big data industry standard as soon as possible and establish a systematic and thorough fund management system for the big data industry. The development of the big data industry will have a leapfrog change on the future development. First, big data technology will enhance the national governance capacity. Through the integration, analysis, summary, and application of historical data between different departments, it will have a positive prediction and prevention effect on public health, national defense security, education and medical care, public transportation, and other aspects. Second, the big data industry will bring about business model innovation. Third, the big data industry will change the way people think. One of the characteristics of the big data industry is that you know what, but not why. That is, we only need to know the final result of the data analysis, and we do not need to know its specific calculation process.



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*Source: 1972726-2022-09-15.xml*