# Evaluation Method of Economic Growth Quality of Tourism-Driven Real Estate Based on AHP

Authors: Qingmin Li
Journal: Wireless Communications and Mobile Computing
 (2022)

---

## Abstract

The traditional tourism-driven real estate economic growth quality evaluation model has the problem of low fitting degree and dynamic degree. In order to solve this problem, a tourism-driven real estate economic growth quality evaluation method based on an analytic hierarchy process (AHP) is proposed. In order to reduce the solution scale of tourism-driven real estate economic growth quality evaluation, the analytic hierarchy process is used to calculate the possibility index, severity index, and economic growth quality index of tourism-driven real estate economic growth and screen and rank the economic growth quality. The discrete zoning model is used to determine the economic growth level of tourism-driven real estate. Using the Mahalanobis distance of multiattribute vector of economic growth quality evaluation index, the distance from the risk element to positive ideal solution and negative ideal solution is defined. By calculating the closeness of data economic growth quality element, this paper constructs a tourism-driven real estate economic growth quality evaluation model. The experimental results show that the model has high dynamics and reliability and greatly improves the joint evaluation effect of tourism-driven real estate economic growth quality.


---

## Body

## 1. Introduction

Due to the great differences in resource endowment, traffic location, realistic foundation, human capital, and openness of the real estate industry, there must be imbalance in promoting economic growth [1], in order to reflect the economic growth level of different types of real estate industries, reveal the characteristics and influencing factors of real estate economic growth differences, so as to provide basis for formulating macrocontrol policies and guiding regional coordinated development, and carry out statistical evaluation of economic growth quality [2, 3].Reference [4] examines the convergence of military expenditure and economic growth in 35 African countries between 1990 and 2015. After the robustness test, the final club classification results do support the hypothesis of club convergence of two variables. The empirical results further show that regional economic communities (RECs) have formed different convergence and divergence clubs, showing considerable heterogeneity in basic defense and growth models. However, this method does not deeply analyze the factors affecting economic growth, resulting in limitations in the evaluation results. Reference [5] simulates the relationship between trust and economic growth in two artificial societies. One artificial society (New Zealand) showed a high initial level of trust, and the other (Argentina) showed a low level of trust. Representative survey data (using the global trust list) sets the initial starting point of the simulation. Computational simulation relies on a rule-based model (RBM) and integrates time through the random simulation algorithm implemented in piskas. Agents in the artificial society are distributed according to the proportion of four trust modes, and there are more high trusters (HTS) in New Zealand. In each iteration, the agent acts as a prisoner’s dilemma, making money or losing money according to different payment matrices, cooperation probability, and interaction frequency and simulating different economic exchange conditions. This method provides a reference for economic evaluation in different regions. Reference [6] proposed a credit risk measurement model based on the improved GSO (glow worm warfare) and elm (extreme learning machine) integration algorithm. Firstly, according to the growth and development characteristics of small and micro enterprises in the big data environment, this paper analyzes the formation mechanism of credit risk of small and micro enterprises from the global perspective of granularity scale, cross-border correlation, and big data drive and establishes a comprehensive credit evaluation index system by summarizing and analyzing the factors affecting credit evaluation indexes. Secondly, a new algorithm is proposed by integrating the advantage set adaptive firefly swarm optimization algorithm with the limit learning machine. Finally, the integrated algorithm based on improved GSO and elm is applied to the credit risk measurement modeling of small and micro enterprises, the sample data of small and micro enterprises are collected, and the simulation experiment is carried out with the help of MATLAB software tools. This method provides a reference for solving the problem of credit risk measurement of small and micro enterprises, but the evaluation effect on tourism-driven real estate is not known. Reference [7] uses the ARDL framework to study the relationship between institutional quality and growth in the context of 27 postsocialist economies. This framework solves the potential endogenous problems. However, the evaluation results of this method have limitations.The analytic hierarchy process can be used for the systematic evaluation of unstructured characteristics and the characteristics of systematic evaluation of multiobjective, multicriteria, and multiperiod [8, 9], extract the variables in the process of tourism-driven real estate economic development, determine the statistical evaluation model indicators and weight values of tourism-driven real estate economic growth, quantify the evaluation index results of the model, and realize the quality evaluation of tourism-driven real estate economic growth. Experiments show that the new evaluation method effectively improves the evaluation accuracy, and the statistical evaluation results are easier to be recognized.


## 2. Construct the Quality Evaluation Index System of Tourism-Driven Real Estate Economic Growth

The construction of a scientific statistical evaluation index system must be based on the connotation and characteristics of economic growth. This paper holds that the connotation and characteristics of tourism-driven real estate economic growth can be grasped from the following five aspects: first, the growth level or maintaining an appropriate growth level [10, 11] is an important embodiment of realizing economic growth; second, structural optimization or promoting structural optimization is the basic path to achieve economic growth; third, efficiency reform or promoting efficiency reform is the key means to achieve economic growth; fourth, green development or adhering to green development is the internal requirement to achieve high-quality economic development; fifth, shared development is the fundamental goal of achieving high-quality economic development.


### 2.1. Basic Principles for Constructing the Quality Evaluation Index System of Tourism-Driven Real Estate Economic Growth

Indicators are elements used to describe certain attributes of objective things. They are not only a specific and behavioral evaluation criterion but also a unit to measure objectives [12]. In the process of evaluating the quality of tourism-driven real estate economic growth, the evaluation index system is the basic basis for evaluating the quality of tourism-driven real estate economic growth. Whether the constructed evaluation index system is scientific and reasonable directly affects the rationality and effectiveness of the evaluation results of tourism-driven real estate economic growth quality [13, 14]. This paper constructs the evaluation system according to the following principles.


#### 2.1.1. Scientific Principle

The scientific principle refers to that in the process of evaluating the quality of tourism-driven real estate economic growth, the evaluation index system should have a certain theoretical basis, and each index should be consistent with the predetermined goal. Secondly, the concept description of each index should be scientific and accurate, the calculation scope should be clear, and the indexes closely related to the quality of tourism-driven real estate economic growth should be comprehensively analyzed and selected, so that the index system can reasonably and effectively reflect the essential characteristics of the evaluation object as much as possible.


#### 2.1.2. Feasibility Principle

The feasibility principle is that in the design process of the tourism-driven real estate economic growth quality evaluation index system, the evaluation index is required to be simple and clear, the index content should have clear connotation and measurability, and the index data should be easy to obtain [15] and simple to process, so as to ensure the smooth progress of the whole evaluation work.


#### 2.1.3. Principle of Comparability

The principle of comparability requires that the evaluation index must be the common attribute of all evaluation objects, reflecting the qualitative consistency. In addition, since the quantities of different things can be compared with each other only after they are transformed into the same unit, the meaning, scope, and unit of measurement of evaluation indicators must be consistent, so as to be comparable. The stronger their comparability is, the more credible the final evaluation result will be.


#### 2.1.4. Accuracy Principle

The principle of accuracy means that when selecting evaluation indicators, we should follow the objective law of tourism-driven real estate economic growth quality activities, combined with the objective reality of tourism-driven real estate economic growth quality activities, and reflect the essence of tourism-driven real estate economic growth quality work, and the selected evaluation indicators should have accurate connotation and extension. It can accurately reflect the general situation of the actual economic growth quality of tourism-driven real estate. The established index system should be objective and credible and can accurately reflect the real level of the evaluation of the economic growth quality of tourism-driven real estate.


#### 2.1.5. Principle of Independence

The principle of independence means that the indexes in the evaluation index system of tourism-driven real estate economic growth quality should maintain a certain relative independence and cannot overlap or subordinate to each other. The indexes at the same level can only be in parallel and cannot exist in the relationship between inclusion and inclusion or causality [16]. The reason is that if the indicators in the index system are not independent of each other, there will be redundant indicators, which will increase the workload of the whole evaluation process and reduce the feasibility of the evaluation results. In addition, if the indicators are included, the indicators will be scored many times in the specific evaluation process. This will increase its corresponding weight and affect the final evaluation results.


### 2.2. Extracting Statistical Evaluation Variables

Based on the connotation and characteristics of economic growth, through the analysis of the influencing factors of the statistical evaluation of tourism-driven real estate economic growth, it is concluded that the factors affecting the effectiveness of the results are produced in the process of statistical evaluation. Therefore, the first step of the statistical evaluation model of tourism-driven real estate economic growth is to extract the statistical evaluation variables [17]. Under the constraints of statistical evaluators and methods, combined with the comparison between the final results of statistical evaluation and the actual development of high-quality economy, the statistical evaluation variables of tourism-driven real estate economic growth are obtained as shown in Table 1.Table 1
Statistical evaluation variables of tourism-driven real estate economic growth.
NumberStatistical evaluation variablesDefinitionVariable characteristic type1Statistical evaluation timeTotal time from the beginning of statistical evaluation to the stage of obtaining statistical evaluation resultsRation2Statistical evaluation timesNumber of statistical evaluations on different aspects of tourism-driven real estate economyRation3Record the statistical evaluation resultsStatistical evaluation results under constraintsRation4Statistical evaluation methodSpecific methods for statistical evaluation of tourism-driven real estate economic growthQualitative5Statistical evaluatorPersonnel performing statistical evaluationQualitativeIn Table1, among the statistical evaluation variables of tourism-driven real estate economic growth, quantitative variables are mainly obtained through various numerical records in the process of statistical evaluation, without manual intervention. Qualitative variables need to be valued by the internal auditors of the statistical evaluation department according to different qualitative variables and the corresponding relationship of evaluation indicators, and they need to be evaluated manually.


### 2.3. Establishment of Evaluation Index System

Based on the above connotation and characteristics of economic growth and following the design principles of scientific, integrated, hierarchical, comparable, and operable statistical evaluation index system [18, 19], the statistical evaluation index system of tourism-driven real estate economic growth is shown in Table 2.Table 2
Statistical evaluation index system of tourism-driven real estate economic growth.
Primary indexSecondary indexReal estate investmentRentProperty appreciationTax reductionIncrease in equityDevelopment investmentSales revenue


## 2.1. Basic Principles for Constructing the Quality Evaluation Index System of Tourism-Driven Real Estate Economic Growth

Indicators are elements used to describe certain attributes of objective things. They are not only a specific and behavioral evaluation criterion but also a unit to measure objectives [12]. In the process of evaluating the quality of tourism-driven real estate economic growth, the evaluation index system is the basic basis for evaluating the quality of tourism-driven real estate economic growth. Whether the constructed evaluation index system is scientific and reasonable directly affects the rationality and effectiveness of the evaluation results of tourism-driven real estate economic growth quality [13, 14]. This paper constructs the evaluation system according to the following principles.


### 2.1.1. Scientific Principle

The scientific principle refers to that in the process of evaluating the quality of tourism-driven real estate economic growth, the evaluation index system should have a certain theoretical basis, and each index should be consistent with the predetermined goal. Secondly, the concept description of each index should be scientific and accurate, the calculation scope should be clear, and the indexes closely related to the quality of tourism-driven real estate economic growth should be comprehensively analyzed and selected, so that the index system can reasonably and effectively reflect the essential characteristics of the evaluation object as much as possible.


### 2.1.2. Feasibility Principle

The feasibility principle is that in the design process of the tourism-driven real estate economic growth quality evaluation index system, the evaluation index is required to be simple and clear, the index content should have clear connotation and measurability, and the index data should be easy to obtain [15] and simple to process, so as to ensure the smooth progress of the whole evaluation work.


### 2.1.3. Principle of Comparability

The principle of comparability requires that the evaluation index must be the common attribute of all evaluation objects, reflecting the qualitative consistency. In addition, since the quantities of different things can be compared with each other only after they are transformed into the same unit, the meaning, scope, and unit of measurement of evaluation indicators must be consistent, so as to be comparable. The stronger their comparability is, the more credible the final evaluation result will be.


### 2.1.4. Accuracy Principle

The principle of accuracy means that when selecting evaluation indicators, we should follow the objective law of tourism-driven real estate economic growth quality activities, combined with the objective reality of tourism-driven real estate economic growth quality activities, and reflect the essence of tourism-driven real estate economic growth quality work, and the selected evaluation indicators should have accurate connotation and extension. It can accurately reflect the general situation of the actual economic growth quality of tourism-driven real estate. The established index system should be objective and credible and can accurately reflect the real level of the evaluation of the economic growth quality of tourism-driven real estate.


### 2.1.5. Principle of Independence

The principle of independence means that the indexes in the evaluation index system of tourism-driven real estate economic growth quality should maintain a certain relative independence and cannot overlap or subordinate to each other. The indexes at the same level can only be in parallel and cannot exist in the relationship between inclusion and inclusion or causality [16]. The reason is that if the indicators in the index system are not independent of each other, there will be redundant indicators, which will increase the workload of the whole evaluation process and reduce the feasibility of the evaluation results. In addition, if the indicators are included, the indicators will be scored many times in the specific evaluation process. This will increase its corresponding weight and affect the final evaluation results.


## 2.1.1. Scientific Principle

The scientific principle refers to that in the process of evaluating the quality of tourism-driven real estate economic growth, the evaluation index system should have a certain theoretical basis, and each index should be consistent with the predetermined goal. Secondly, the concept description of each index should be scientific and accurate, the calculation scope should be clear, and the indexes closely related to the quality of tourism-driven real estate economic growth should be comprehensively analyzed and selected, so that the index system can reasonably and effectively reflect the essential characteristics of the evaluation object as much as possible.


## 2.1.2. Feasibility Principle

The feasibility principle is that in the design process of the tourism-driven real estate economic growth quality evaluation index system, the evaluation index is required to be simple and clear, the index content should have clear connotation and measurability, and the index data should be easy to obtain [15] and simple to process, so as to ensure the smooth progress of the whole evaluation work.


## 2.1.3. Principle of Comparability

The principle of comparability requires that the evaluation index must be the common attribute of all evaluation objects, reflecting the qualitative consistency. In addition, since the quantities of different things can be compared with each other only after they are transformed into the same unit, the meaning, scope, and unit of measurement of evaluation indicators must be consistent, so as to be comparable. The stronger their comparability is, the more credible the final evaluation result will be.


## 2.1.4. Accuracy Principle

The principle of accuracy means that when selecting evaluation indicators, we should follow the objective law of tourism-driven real estate economic growth quality activities, combined with the objective reality of tourism-driven real estate economic growth quality activities, and reflect the essence of tourism-driven real estate economic growth quality work, and the selected evaluation indicators should have accurate connotation and extension. It can accurately reflect the general situation of the actual economic growth quality of tourism-driven real estate. The established index system should be objective and credible and can accurately reflect the real level of the evaluation of the economic growth quality of tourism-driven real estate.


## 2.1.5. Principle of Independence

The principle of independence means that the indexes in the evaluation index system of tourism-driven real estate economic growth quality should maintain a certain relative independence and cannot overlap or subordinate to each other. The indexes at the same level can only be in parallel and cannot exist in the relationship between inclusion and inclusion or causality [16]. The reason is that if the indicators in the index system are not independent of each other, there will be redundant indicators, which will increase the workload of the whole evaluation process and reduce the feasibility of the evaluation results. In addition, if the indicators are included, the indicators will be scored many times in the specific evaluation process. This will increase its corresponding weight and affect the final evaluation results.


## 2.2. Extracting Statistical Evaluation Variables

Based on the connotation and characteristics of economic growth, through the analysis of the influencing factors of the statistical evaluation of tourism-driven real estate economic growth, it is concluded that the factors affecting the effectiveness of the results are produced in the process of statistical evaluation. Therefore, the first step of the statistical evaluation model of tourism-driven real estate economic growth is to extract the statistical evaluation variables [17]. Under the constraints of statistical evaluators and methods, combined with the comparison between the final results of statistical evaluation and the actual development of high-quality economy, the statistical evaluation variables of tourism-driven real estate economic growth are obtained as shown in Table 1.Table 1
Statistical evaluation variables of tourism-driven real estate economic growth.
NumberStatistical evaluation variablesDefinitionVariable characteristic type1Statistical evaluation timeTotal time from the beginning of statistical evaluation to the stage of obtaining statistical evaluation resultsRation2Statistical evaluation timesNumber of statistical evaluations on different aspects of tourism-driven real estate economyRation3Record the statistical evaluation resultsStatistical evaluation results under constraintsRation4Statistical evaluation methodSpecific methods for statistical evaluation of tourism-driven real estate economic growthQualitative5Statistical evaluatorPersonnel performing statistical evaluationQualitativeIn Table1, among the statistical evaluation variables of tourism-driven real estate economic growth, quantitative variables are mainly obtained through various numerical records in the process of statistical evaluation, without manual intervention. Qualitative variables need to be valued by the internal auditors of the statistical evaluation department according to different qualitative variables and the corresponding relationship of evaluation indicators, and they need to be evaluated manually.


## 2.3. Establishment of Evaluation Index System

Based on the above connotation and characteristics of economic growth and following the design principles of scientific, integrated, hierarchical, comparable, and operable statistical evaluation index system [18, 19], the statistical evaluation index system of tourism-driven real estate economic growth is shown in Table 2.Table 2
Statistical evaluation index system of tourism-driven real estate economic growth.
Primary indexSecondary indexReal estate investmentRentProperty appreciationTax reductionIncrease in equityDevelopment investmentSales revenue


## 3. Design of Tourism-Driven Real Estate Economic Growth Quality Evaluation Model

Based on the statistical evaluation index system of tourism-driven real estate economic growth constructed above, a tourism-driven real estate economic growth quality evaluation model is constructed based on the AHP method.


### 3.1. Theoretical Basis of HP Method

The analytic hierarchy process refers to taking a complex multiobjective decision-making problem as a system, decomposing the goal into multiple goals or criteria, then decomposing it into several levels of multiple indicators (or criteria and constraints), and calculating the hierarchical single ranking (weight) and total ranking through the fuzzy quantitative method of qualitative indicators as the goal (multiple indicators) or a systematic method of multischeme optimization decision [20, 21]. The analytic hierarchy process decomposes the decision-making problem into different hierarchical structures according to the order of general objectives, subobjectives of each level, evaluation criteria, and specific standby investment scheme and then obtains the priority weight of each element of each level to an element of the upper level by solving the eigenvector of the judgment matrix. Finally, the method of reweighting sum is used to merge the final weight of each alternative scheme to the overall goal, and the best scheme is the one with the largest final weight.The calculation steps of the analytic hierarchy process mainly include the following contents.


#### 3.1.1. Establish Hierarchical Structure Model

The decision-making objectives, factors considered (decision-making criteria), and decision-making objects are divided into the highest level, middle level, and lowest level according to their relationship, and the hierarchical structure diagram is drawn. The highest level refers to the purpose of decision-making and the problems to be solved. The lowest level refers to the alternatives in decision-making. The interlayer refers to the factors considered and the criteria for decision-making. For the two adjacent layers, the high layer is called the target layer and the low layer is the factor layer.


#### 3.1.2. Construct Judgment (Pairwise Comparison) Matrix

When determining the weight of each factor at each level, if it is only a qualitative result, it is often not easy to be accepted by others. Therefore, the consistent matrix method [22, 23] is used; that is, all factors are not compared together but compared with each other. At this time, the relative scale is adopted to reduce the difficulty of comparing various factors with different properties as much as possible, so as to improve the accuracy. For example, for a certain criterion, each scheme under it shall be compared in pairs, and the grade shall be evaluated according to its importance.


#### 3.1.3. Hierarchical Single Sorting and Its Consistency Test

The eigenvector corresponding to the maximum eigenvalue of the judgment matrix is normalized (so that the sum of the elements in the vector is equal to 1) and then recorded asw. The element of W is the ranking weight of the relative importance of the factors at the same level to the factors at the previous level. This process is called hierarchical single ranking. Consistency check is required to confirm whether hierarchical order sorting can be performed. The smaller the consistency index, the greater the consistency. If the test coefficient is less than 0.1, it is considered that the judgment matrix passes the consistency test; otherwise, it does not have satisfactory consistency.


#### 3.1.4. Hierarchical General Ranking and Its Consistency Test

Calculating the weight of the relative importance of all factors at a certain level to the highest level (overall goal) is called hierarchical total ranking. This process is carried out from the highest level to the lowest level.


### 3.2. Screening and Ranking Tourism-Driven Real Estate Economic Growth

According to the possibility indicators, severity indicators, and economic growth quality indicators of tourism-driven real estate economic growth, the quality indicators of economic growth are screened and sorted. The possibility indicators are usually used to characterize the probability of exceeding the limit of economic growth pressure of tourism-driven real estate when there are risks in tourism-driven real estate economy [24, 25]. The calculation formula is
(1)δLikeS=maxPrS>Smax,PrS<Smin,where Pr⋅ represents the probability value and S represents the pressure of tourism-driven real estate economic growth.The severity index is used to characterize the severity of the exceeding limit of the economic growth pressure of tourism-driven real estate when there are risks in the tourism-driven real estate economy [26], and the calculation formula is
(2)δSeveS=maxS−SmaxSmax,S−SminSmin.The quality index of economic growth is used to represent the total risk of exceeding the limit of the economic growth pressure of tourism-driven real estate when there are risks in the tourism-driven real estate economy. The calculation formula is(3)δRiskE=∑EδLikeSδSeveS.In order to reduce the solution scale of the economic growth quality evaluation of tourism-driven real estate, the analytic hierarchy process is applied to the screening and ranking of tourism-driven real estate economic growth [27], and the factors that have little impact on the economic growth quality of tourism-driven real estate economy are screened and deleted.The probability of economic growth quality is defined as(4)α=PPmax,where Pmax represents the quality limit of economic growth, P represents the quality of real-time economic growth, and α represents the quality rate of economic growth.For tourism-driven real estate, when screening and ranking the quality of economic growth, considering the possible probability of each expected quality of economic growth and the severity of the risk consequences of tourism-driven real estate economies, multiply the probability of economic growth quality of tourism-driven real estate economy with the quality of real-time economic growth [28, 29] and take the calculation result as the screening and ranking index δRI of tourism-driven real estate economic growth, expressed as
(5)δRI=P⋅α.The screening and ranking process of tourism-driven real estate economic growth is shown in Figure1.Figure 1
Screening and ranking process of tourism-driven real estate economic growth.In order to reduce the solution scale of tourism-driven real estate economic growth quality evaluation, the possibility index, severity index, and economic growth quality index of tourism-driven real estate economic growth are calculated by the analytic hierarchy process, and the influencing factors of tourism-driven real estate economic growth quality are screened and sorted.


### 3.3. Determine the Economic Growth Level of Tourism-Driven Real Estate

Before determining the economic growth level of tourism-driven real estate, first, establish the deviation degree model of the economic growth scale of tourism-driven real estate, so that the distance between the economic growth model of tourism-driven real estate and the white model base of industrial economic growth iswDis and the distance between the economic growth model of tourism-driven real estate and the gray model base of industrial economic growth is bDis. Then, the deviation degree model of the tourism-driven real estate economic growth scale can be defined as
(6)DEV=1+wDis−bDismaxwDis,bDis×0.5.The specific steps are the following.Step 1.
Design the economic similarity measurement method of tourism-driven real estate and quantify the deviation degree of economic growth scale of tourism-driven real estate.
Based on the economic growth model in the industry of tourism-driven real estate, the distance between the economic growth model of tourism-driven real estate and the white model base and gray model base is measured by defining the distance measurement formula, and two distance sets are obtained, namely,(7)wDisList=Dis1,Dis2,⋯,Dist,bDisList=Dis1,Dis2,⋯,Disq.Step 2.
Using thek nearest neighbor matching measurement method [30], find the economic growth model with the smallest distance in wDis and bDis, calculate its weighted average distance, and take it as the distance between tourism-driven real estate and white model library and gray model library.Step 3.
Based onwDis and bDis, calculate the deviation degree of economic growth scale of tourism-driven real estate according to the deviation degree model of economic growth scale of tourism-driven real estate.In the deviation degree model of tourism-driven real estate, according to the past overdue information, the correlation between the deviation degree and the quality level of economic growth is established, and the discrete zoning model of overdue risk of industrial economic growth is created to realize the quality of economic growth and its evaluation after the enterprise deviation degree of self-evolution ability reaches level 4.For the deviation degree model of tourism-driven real estate and on the basis of historical overdue information, extract the economic growth data of tourism-driven real estate in recent years as the training template of the discrete zoning model [31]. Take the economic growth data of each Z month as the data set of the economic growth model of tourism-driven real estate, calculate the deviation degree of the enterprise in the next month through the deviation degree model of tourism-driven real estate, and then conduct a sample pilot on the historical data generated by the enterprise in the corresponding month to generate a binary {deviation degree, true expectation or not}, to train discrete partitions.According to the binary set generated by tourism-driven real estate, a discrete zoning model of economic growth quality is established. Suppose that the known binary sample set isp, and the sample set with the true overdue of T in the sample set is TP. Based on the overdue hit rate, underreporting rate, and high-risk proportion of industrial economic growth, a discrete zoning model of deviation degree is established, which is the distribution and fitting of a priori knowledge points [32, 33].According to the above steps, the quality of economic growth is divided into four levels: high, very high, and medium low according to the discrete zoning of the deviation degree of tourism-driven real estate. The smaller the miss ratio and the larger the hit ratio, the better the discrete zoning model.By defining the deviation degree model of the tourism-driven real estate economic growth scale, this paper calculates the deviation degree of the tourism-driven real estate economic growth scale and determines the economic growth level of tourism-driven real estate by using the discrete zoning model of tourism-driven real estate economic growth.


### 3.4. Evaluation of Tourism-Driven Real Estate Economic Growth

Aiming at the problem of distance statistics between the attributes of tourism-driven real estate economic growth quality evaluation indicators, the analytic hierarchy process is used to avoid the dimensionalization problem between the attributes of different evaluation indicators [34] and eliminate the mutual interference between different evaluation indicators. It is assumed that there is a multiattribute vector x=x1,x2,⋯,xp of evaluation indicators, whose mean value is μ=μ1,μ2,⋯,μp, and s represents the covariance matrix of x. Then, the Mahalanobis distance of the multiattribute vector of the evaluation index is
(8)Mx=x−μTs−1x−μ.LetAi=ai11,ai21,ai31,ai41,ai51 represent the index value corresponding to the economic growth quality element of the ith economic growth data after accurate processing, B+=b1+,b2+,b3+,b4+,b5+ represent the positive ideal solution, B−=b1−,b2−,b3−,b4−,b5− represent the negative ideal solution, and s−1 represent the inverse matrix of the A covariance matrix of the economic growth quality sample of economic growth data [35]. Since s−1 is invariant to all linear transformations, it will not be affected by the scalar dimensioning of the evaluation index, and the correlation between different evaluation index attributes can be eliminated. The distance from the economic growth quality element of the economic growth data to B+ and B− is defined as
(9)MAi,B+=Ai−B+Ts−1Ai−B+,(10)MAi,B−=Ai−B−Ts−1Ai−B−.Assuming that the passing progress of theith economic growth data economic growth quality element is ci, the calculation formula is
(11)ci=MAi,B−MAi,B−+MAi,B+.Sort out the posting progress indicators of quality elements in different evaluation dimensions, process the evaluation parameters in different dimensions into data samples, and process the data samples into independent status. The processing process can be expressed as follows:(12)W1=n1n1+12,W2=n2n2+12,where W1 and W2 represent independent samples after processing and n1 and n2 represent data capacity. Call the discrete processing process to approximate the observation parameters in the test data sample. The processing process can be expressed as
(13)G=n1n1+1/2+n2n2+1/2M,where G represents the observation parameter, and the meaning of other parameters remains unchanged. In order to realize the continuous correction of independent quantitative index parameters, set the data statistics in the numerical rejection field, which can be expressed as
(14)J=GSw1/n1+1/n2,where Sw represents independent parameters, and the meaning of other parameters remains unchanged. When controlling the correlation between parameter values and corresponding indicators, sort out the value sudden change interval generated by statistics during the period of statistic value change, as shown in Figure 2.Figure 2
Numerical sudden change interval.Corresponding to the numerical sudden change interval shown in Figure2, sort out the data sudden change interval into three numerical levels, quantify the cultivation effect index of the same level as the same effect parameter value, and construct the effect automatic evaluation process under the control of this value.Based on the above analysis, the joint assessment steps of tourism-driven real estate economic growth can be designed as follows.Step 1.
Transform the economic growth quality evaluation language given by experts into trapezoidal fuzzy data [36], calculate the fuzzy decision matrix of economic growth quality tij of each economic growth data, and obtain the tourism-driven real estate economic growth quality evaluation matrix.Step 2.
After calculating each matrix, then calculate the risk matrix of each economic growth data economic growth quality element to form a group decision evaluation matrix of economic growth quality.Step 3.
Standardize the group decision-making evaluation matrix for the quality of economic growth and deal with it accurately.Step 4.
Find out the positive ideal solution and negative ideal solution of tourism-driven real estate economy under the quality of economic growth by calculating the covariance matrix of economic growth data and economic growth quality decision matrix.Step 5.
Calculate the distance between the economic growth quality element of each economic growth data andB+ and B− according to formulas (9) and (10).Step 6.
Use formula (11) to rank the quality elements of economic growth in the final economic growth data, so as to realize the joint evaluation of tourism-driven real estate economic growth.To sum up, the Mahalanobis distance of multiattribute vector of economic growth quality evaluation index is used to define the distance from risk element to positive ideal solution and negative ideal solution. By calculating the closeness of tourism-driven real estate economic growth element, the tourism-driven real estate economic growth quality evaluation model is constructed to realize the joint evaluation of tourism-driven real estate economic growth.


## 3.1. Theoretical Basis of HP Method

The analytic hierarchy process refers to taking a complex multiobjective decision-making problem as a system, decomposing the goal into multiple goals or criteria, then decomposing it into several levels of multiple indicators (or criteria and constraints), and calculating the hierarchical single ranking (weight) and total ranking through the fuzzy quantitative method of qualitative indicators as the goal (multiple indicators) or a systematic method of multischeme optimization decision [20, 21]. The analytic hierarchy process decomposes the decision-making problem into different hierarchical structures according to the order of general objectives, subobjectives of each level, evaluation criteria, and specific standby investment scheme and then obtains the priority weight of each element of each level to an element of the upper level by solving the eigenvector of the judgment matrix. Finally, the method of reweighting sum is used to merge the final weight of each alternative scheme to the overall goal, and the best scheme is the one with the largest final weight.The calculation steps of the analytic hierarchy process mainly include the following contents.


### 3.1.1. Establish Hierarchical Structure Model

The decision-making objectives, factors considered (decision-making criteria), and decision-making objects are divided into the highest level, middle level, and lowest level according to their relationship, and the hierarchical structure diagram is drawn. The highest level refers to the purpose of decision-making and the problems to be solved. The lowest level refers to the alternatives in decision-making. The interlayer refers to the factors considered and the criteria for decision-making. For the two adjacent layers, the high layer is called the target layer and the low layer is the factor layer.


### 3.1.2. Construct Judgment (Pairwise Comparison) Matrix

When determining the weight of each factor at each level, if it is only a qualitative result, it is often not easy to be accepted by others. Therefore, the consistent matrix method [22, 23] is used; that is, all factors are not compared together but compared with each other. At this time, the relative scale is adopted to reduce the difficulty of comparing various factors with different properties as much as possible, so as to improve the accuracy. For example, for a certain criterion, each scheme under it shall be compared in pairs, and the grade shall be evaluated according to its importance.


### 3.1.3. Hierarchical Single Sorting and Its Consistency Test

The eigenvector corresponding to the maximum eigenvalue of the judgment matrix is normalized (so that the sum of the elements in the vector is equal to 1) and then recorded asw. The element of W is the ranking weight of the relative importance of the factors at the same level to the factors at the previous level. This process is called hierarchical single ranking. Consistency check is required to confirm whether hierarchical order sorting can be performed. The smaller the consistency index, the greater the consistency. If the test coefficient is less than 0.1, it is considered that the judgment matrix passes the consistency test; otherwise, it does not have satisfactory consistency.


### 3.1.4. Hierarchical General Ranking and Its Consistency Test

Calculating the weight of the relative importance of all factors at a certain level to the highest level (overall goal) is called hierarchical total ranking. This process is carried out from the highest level to the lowest level.


## 3.1.1. Establish Hierarchical Structure Model

The decision-making objectives, factors considered (decision-making criteria), and decision-making objects are divided into the highest level, middle level, and lowest level according to their relationship, and the hierarchical structure diagram is drawn. The highest level refers to the purpose of decision-making and the problems to be solved. The lowest level refers to the alternatives in decision-making. The interlayer refers to the factors considered and the criteria for decision-making. For the two adjacent layers, the high layer is called the target layer and the low layer is the factor layer.


## 3.1.2. Construct Judgment (Pairwise Comparison) Matrix

When determining the weight of each factor at each level, if it is only a qualitative result, it is often not easy to be accepted by others. Therefore, the consistent matrix method [22, 23] is used; that is, all factors are not compared together but compared with each other. At this time, the relative scale is adopted to reduce the difficulty of comparing various factors with different properties as much as possible, so as to improve the accuracy. For example, for a certain criterion, each scheme under it shall be compared in pairs, and the grade shall be evaluated according to its importance.


## 3.1.3. Hierarchical Single Sorting and Its Consistency Test

The eigenvector corresponding to the maximum eigenvalue of the judgment matrix is normalized (so that the sum of the elements in the vector is equal to 1) and then recorded asw. The element of W is the ranking weight of the relative importance of the factors at the same level to the factors at the previous level. This process is called hierarchical single ranking. Consistency check is required to confirm whether hierarchical order sorting can be performed. The smaller the consistency index, the greater the consistency. If the test coefficient is less than 0.1, it is considered that the judgment matrix passes the consistency test; otherwise, it does not have satisfactory consistency.


## 3.1.4. Hierarchical General Ranking and Its Consistency Test

Calculating the weight of the relative importance of all factors at a certain level to the highest level (overall goal) is called hierarchical total ranking. This process is carried out from the highest level to the lowest level.


## 3.2. Screening and Ranking Tourism-Driven Real Estate Economic Growth

According to the possibility indicators, severity indicators, and economic growth quality indicators of tourism-driven real estate economic growth, the quality indicators of economic growth are screened and sorted. The possibility indicators are usually used to characterize the probability of exceeding the limit of economic growth pressure of tourism-driven real estate when there are risks in tourism-driven real estate economy [24, 25]. The calculation formula is
(1)δLikeS=maxPrS>Smax,PrS<Smin,where Pr⋅ represents the probability value and S represents the pressure of tourism-driven real estate economic growth.The severity index is used to characterize the severity of the exceeding limit of the economic growth pressure of tourism-driven real estate when there are risks in the tourism-driven real estate economy [26], and the calculation formula is
(2)δSeveS=maxS−SmaxSmax,S−SminSmin.The quality index of economic growth is used to represent the total risk of exceeding the limit of the economic growth pressure of tourism-driven real estate when there are risks in the tourism-driven real estate economy. The calculation formula is(3)δRiskE=∑EδLikeSδSeveS.In order to reduce the solution scale of the economic growth quality evaluation of tourism-driven real estate, the analytic hierarchy process is applied to the screening and ranking of tourism-driven real estate economic growth [27], and the factors that have little impact on the economic growth quality of tourism-driven real estate economy are screened and deleted.The probability of economic growth quality is defined as(4)α=PPmax,where Pmax represents the quality limit of economic growth, P represents the quality of real-time economic growth, and α represents the quality rate of economic growth.For tourism-driven real estate, when screening and ranking the quality of economic growth, considering the possible probability of each expected quality of economic growth and the severity of the risk consequences of tourism-driven real estate economies, multiply the probability of economic growth quality of tourism-driven real estate economy with the quality of real-time economic growth [28, 29] and take the calculation result as the screening and ranking index δRI of tourism-driven real estate economic growth, expressed as
(5)δRI=P⋅α.The screening and ranking process of tourism-driven real estate economic growth is shown in Figure1.Figure 1
Screening and ranking process of tourism-driven real estate economic growth.In order to reduce the solution scale of tourism-driven real estate economic growth quality evaluation, the possibility index, severity index, and economic growth quality index of tourism-driven real estate economic growth are calculated by the analytic hierarchy process, and the influencing factors of tourism-driven real estate economic growth quality are screened and sorted.


## 3.3. Determine the Economic Growth Level of Tourism-Driven Real Estate

Before determining the economic growth level of tourism-driven real estate, first, establish the deviation degree model of the economic growth scale of tourism-driven real estate, so that the distance between the economic growth model of tourism-driven real estate and the white model base of industrial economic growth iswDis and the distance between the economic growth model of tourism-driven real estate and the gray model base of industrial economic growth is bDis. Then, the deviation degree model of the tourism-driven real estate economic growth scale can be defined as
(6)DEV=1+wDis−bDismaxwDis,bDis×0.5.The specific steps are the following.Step 1.
Design the economic similarity measurement method of tourism-driven real estate and quantify the deviation degree of economic growth scale of tourism-driven real estate.
Based on the economic growth model in the industry of tourism-driven real estate, the distance between the economic growth model of tourism-driven real estate and the white model base and gray model base is measured by defining the distance measurement formula, and two distance sets are obtained, namely,(7)wDisList=Dis1,Dis2,⋯,Dist,bDisList=Dis1,Dis2,⋯,Disq.Step 2.
Using thek nearest neighbor matching measurement method [30], find the economic growth model with the smallest distance in wDis and bDis, calculate its weighted average distance, and take it as the distance between tourism-driven real estate and white model library and gray model library.Step 3.
Based onwDis and bDis, calculate the deviation degree of economic growth scale of tourism-driven real estate according to the deviation degree model of economic growth scale of tourism-driven real estate.In the deviation degree model of tourism-driven real estate, according to the past overdue information, the correlation between the deviation degree and the quality level of economic growth is established, and the discrete zoning model of overdue risk of industrial economic growth is created to realize the quality of economic growth and its evaluation after the enterprise deviation degree of self-evolution ability reaches level 4.For the deviation degree model of tourism-driven real estate and on the basis of historical overdue information, extract the economic growth data of tourism-driven real estate in recent years as the training template of the discrete zoning model [31]. Take the economic growth data of each Z month as the data set of the economic growth model of tourism-driven real estate, calculate the deviation degree of the enterprise in the next month through the deviation degree model of tourism-driven real estate, and then conduct a sample pilot on the historical data generated by the enterprise in the corresponding month to generate a binary {deviation degree, true expectation or not}, to train discrete partitions.According to the binary set generated by tourism-driven real estate, a discrete zoning model of economic growth quality is established. Suppose that the known binary sample set isp, and the sample set with the true overdue of T in the sample set is TP. Based on the overdue hit rate, underreporting rate, and high-risk proportion of industrial economic growth, a discrete zoning model of deviation degree is established, which is the distribution and fitting of a priori knowledge points [32, 33].According to the above steps, the quality of economic growth is divided into four levels: high, very high, and medium low according to the discrete zoning of the deviation degree of tourism-driven real estate. The smaller the miss ratio and the larger the hit ratio, the better the discrete zoning model.By defining the deviation degree model of the tourism-driven real estate economic growth scale, this paper calculates the deviation degree of the tourism-driven real estate economic growth scale and determines the economic growth level of tourism-driven real estate by using the discrete zoning model of tourism-driven real estate economic growth.


## 3.4. Evaluation of Tourism-Driven Real Estate Economic Growth

Aiming at the problem of distance statistics between the attributes of tourism-driven real estate economic growth quality evaluation indicators, the analytic hierarchy process is used to avoid the dimensionalization problem between the attributes of different evaluation indicators [34] and eliminate the mutual interference between different evaluation indicators. It is assumed that there is a multiattribute vector x=x1,x2,⋯,xp of evaluation indicators, whose mean value is μ=μ1,μ2,⋯,μp, and s represents the covariance matrix of x. Then, the Mahalanobis distance of the multiattribute vector of the evaluation index is
(8)Mx=x−μTs−1x−μ.LetAi=ai11,ai21,ai31,ai41,ai51 represent the index value corresponding to the economic growth quality element of the ith economic growth data after accurate processing, B+=b1+,b2+,b3+,b4+,b5+ represent the positive ideal solution, B−=b1−,b2−,b3−,b4−,b5− represent the negative ideal solution, and s−1 represent the inverse matrix of the A covariance matrix of the economic growth quality sample of economic growth data [35]. Since s−1 is invariant to all linear transformations, it will not be affected by the scalar dimensioning of the evaluation index, and the correlation between different evaluation index attributes can be eliminated. The distance from the economic growth quality element of the economic growth data to B+ and B− is defined as
(9)MAi,B+=Ai−B+Ts−1Ai−B+,(10)MAi,B−=Ai−B−Ts−1Ai−B−.Assuming that the passing progress of theith economic growth data economic growth quality element is ci, the calculation formula is
(11)ci=MAi,B−MAi,B−+MAi,B+.Sort out the posting progress indicators of quality elements in different evaluation dimensions, process the evaluation parameters in different dimensions into data samples, and process the data samples into independent status. The processing process can be expressed as follows:(12)W1=n1n1+12,W2=n2n2+12,where W1 and W2 represent independent samples after processing and n1 and n2 represent data capacity. Call the discrete processing process to approximate the observation parameters in the test data sample. The processing process can be expressed as
(13)G=n1n1+1/2+n2n2+1/2M,where G represents the observation parameter, and the meaning of other parameters remains unchanged. In order to realize the continuous correction of independent quantitative index parameters, set the data statistics in the numerical rejection field, which can be expressed as
(14)J=GSw1/n1+1/n2,where Sw represents independent parameters, and the meaning of other parameters remains unchanged. When controlling the correlation between parameter values and corresponding indicators, sort out the value sudden change interval generated by statistics during the period of statistic value change, as shown in Figure 2.Figure 2
Numerical sudden change interval.Corresponding to the numerical sudden change interval shown in Figure2, sort out the data sudden change interval into three numerical levels, quantify the cultivation effect index of the same level as the same effect parameter value, and construct the effect automatic evaluation process under the control of this value.Based on the above analysis, the joint assessment steps of tourism-driven real estate economic growth can be designed as follows.Step 1.
Transform the economic growth quality evaluation language given by experts into trapezoidal fuzzy data [36], calculate the fuzzy decision matrix of economic growth quality tij of each economic growth data, and obtain the tourism-driven real estate economic growth quality evaluation matrix.Step 2.
After calculating each matrix, then calculate the risk matrix of each economic growth data economic growth quality element to form a group decision evaluation matrix of economic growth quality.Step 3.
Standardize the group decision-making evaluation matrix for the quality of economic growth and deal with it accurately.Step 4.
Find out the positive ideal solution and negative ideal solution of tourism-driven real estate economy under the quality of economic growth by calculating the covariance matrix of economic growth data and economic growth quality decision matrix.Step 5.
Calculate the distance between the economic growth quality element of each economic growth data andB+ and B− according to formulas (9) and (10).Step 6.
Use formula (11) to rank the quality elements of economic growth in the final economic growth data, so as to realize the joint evaluation of tourism-driven real estate economic growth.To sum up, the Mahalanobis distance of multiattribute vector of economic growth quality evaluation index is used to define the distance from risk element to positive ideal solution and negative ideal solution. By calculating the closeness of tourism-driven real estate economic growth element, the tourism-driven real estate economic growth quality evaluation model is constructed to realize the joint evaluation of tourism-driven real estate economic growth.


## 4. Experimental Analysis

Randomly select 20 evaluation scores of tourism-driven real estate economic growth quality as the numerical processing object, and the theoretical and practical judgment is 100 points. Take the evaluation score of tourism-driven real estate economic growth quality as the processing object of the evaluation method, process the above evaluation values into a computable data set, and construct the training database structure. Prepare the latest configuration of the upper computer mounted evaluation result database structure, prepare the performance indicators reflected in the evaluation process of the methods in references [4] and [5] and the evaluation methods in this paper, and compare the performance of the three evaluation methods.


### 4.1. Experimental Preparation

In the same host environment, we call the database structure and control the three evaluation methods to deal with the data set. After the evaluation is finished, we call the evaluation indicators of participation in the evaluation and select the same evaluation indicators in the three evaluation methods. According to the evaluation scores of the tourism-driven real estate economic growth quality, the assignment is processed as the following numerical relationship:(15)F=εi2π,i=1,εi2,i=2,εi2π−z,i=N,where F represents the index parameters after assignment processing, z represents the data fusion coefficient, εi represents the mean value of theoretical and practical evaluation results, and i represents the number of times the index participates in the evaluation. Corresponding to the value after the above assignment processing, the evaluation index is constructed, and the correlation judgment numerical relationship between the evaluation indexes is constructed. The numerical relationship can be expressed as
(16)k=1N∑j=1NFjA,where k represents the calculated correlation parameter, Fj represents the index data set after assignment, and A represents the calibration parameter.


### 4.2. Analysis of Experimental Results

According to the correlation value of the indicators calculated above, after repeated iterative calculation for 10 times, sort out the calculated correlation value. Finally, the correlation between the participating evaluation indicators in the three evaluation methods is shown in Figure3.Figure 3
Correlation results of three effect evaluation methods.Corresponding to the constructed correlation value judgment results, when the defined correlation value is infinitely close to 1, it indicates that the indicators selected by this evaluation method are highly correlated. After ten iterations, according to the data points calculated and distributed in Figure3, the correlation parameters calculated in reference [4] are between -2 and 2, and the correlation of the indicators involved in the evaluation process is poor. The correlation value calculated by the method in reference [5] is between -2.5 and 0.8, and the correlation of the selected indicators in the evaluation process is poor. The correlation index calculated in the iterative process of the designed evaluation method tends to be close to the value 1. Compared with the two existing evaluation methods, the correlation index selected in the evaluation process is the strongest.Under the above experimental environment, corresponding to the index parameters of the above assignment processing, calculate theF statistics formed by the index parameters. The numerical relationship of the F statistics can be expressed as
(17)TF=sμ+V,where TF represents the calculated F statistic, μ represents the data test value, V represents the statistic of the parameter, and s represents the score difference parameter. Corresponding to the numerical relationship calculated above, sort out the F statistics calculated by three evaluation methods. The numerical relationship of statistics is shown in Figure 4.Figure 4
Numerical results ofF statistics calculated by three evaluation methods.Under the numerical relationship constructed in Figure4, the data set prepared for the experiment is repeatedly calculated. When the value of F statistic is close to 0.5, it shows that the automatic evaluation method shows strong significance, and the results of this evaluation method are highly scientific. According to the numerical relationship of statistical arrangement, the value of F statistic calculated in reference [4] is between 0.9 and 1.0, and the significance of this evaluation method is poor. The value of F statistic obtained by the method in reference [5] is about 0.2~0.4, and this evaluation method fails to show strong significance. The F statistic calculated by the designed evaluation method is about 0.5. Compared with the applied evaluation method, the evaluation result of this evaluation process is the most scientific.Keep the above experimental environment unchanged, call the index parameters of the above assignment processing, and use the associated Legendre function to construct the evaluation accuracy of the evaluation method. The accuracy value can be expressed as(18)χn=costPid,τn=χnα,where Pi represents the associated Legendre function, χn represents the infinite order function, cost represents the orthogonal curve, d represents the class II parameter of the index, α represents the dimensional scale parameter, and τn represents the value of evaluation accuracy. According to the accuracy numerical relationship of the above structure, sort out the calculated values. The evaluation accuracy results of the three evaluation methods are shown in Figure 5.Figure 5
Accuracy values calculated by three evaluation methods.Corresponding to the precision value relationship calculated in Figure5, the closer the known defined precision value is to 1, the better the precision of this evaluation method. According to the accuracy numerical results shown in Figure 5, the accuracy value in document [4] is 0.4, and the accuracy of the evaluation result is the worst. The accuracy value calculated by the method in reference [5] is 0.6, and the accuracy of the evaluation result is better. The precision calculated by the designed evaluation method is 0.8. Compared with the two selected comparison methods, the designed evaluation method has the best accuracy.The dynamic comparison results of the three tourism-driven real estate economic growth quality joint evaluation models are shown in Figure6.Figure 6
Dynamic comparison results of different models.It can be seen from the results in Figure6 that the economic evaluation method of tourism-driven real estate based on AHP designed in this paper is highly consistent with the time curve, because the evaluation model is based on the possibility index, severity index, and growth quality index of the growth quality of tourism-driven real estate user data before evaluating the economic growth quality of tourism-driven real estate. By screening and ranking the growth quality, this paper effectively analyzes the quality of tourism-driven real estate economic growth, which makes the dynamic degree of the evaluation model better.There is a close relationship between the evaluation results and the reliability of the evaluation results. The more accurate the evaluation results are, the more reliable the evaluation results are. Figure7 shows the comparison results of the reliability of the evaluation results of three different methods.Figure 7
Comparison of reliability of evaluation results of different methods.By analyzing the experimental data in Figure7, it can be seen that the proposed method has higher evaluation accuracy, so the reliability of the evaluation results of the whole method is better. At the same time, the proposed method also focuses on the analysis of the relevant influencing factors of the economic growth quality of tourism-driven real estate and defines the level, and the whole operation process is completed by a computer. It has high reliability. In the actual evaluation process of the other two methods, all the scores are artificially formulated, so the overall reliability is lower.Test the three methods to extract and classify the evaluation indexes of tourism-driven real estate economic growth quality and compare and analyze the classification performance of the evaluation indexes of each method by using the PR curve. The experimental results are shown in Figure8.Figure 8
PR curves of different methods.According to the analysis of Figure8, when the three methods are used to classify the evaluation indexes of tourism-driven real estate economic growth quality, respectively, with the gradual increase of accuracy, the recall index gradually decreases. The maximum accuracy of the reference [4] method is 82%, the maximum accuracy of the reference [5] method can reach 88%, and the maximum accuracy of this method can reach 96%. The results show that the tourism-driven real estate economic growth quality evaluation index of this method has the best classification and recognition ability.


## 4.1. Experimental Preparation

In the same host environment, we call the database structure and control the three evaluation methods to deal with the data set. After the evaluation is finished, we call the evaluation indicators of participation in the evaluation and select the same evaluation indicators in the three evaluation methods. According to the evaluation scores of the tourism-driven real estate economic growth quality, the assignment is processed as the following numerical relationship:(15)F=εi2π,i=1,εi2,i=2,εi2π−z,i=N,where F represents the index parameters after assignment processing, z represents the data fusion coefficient, εi represents the mean value of theoretical and practical evaluation results, and i represents the number of times the index participates in the evaluation. Corresponding to the value after the above assignment processing, the evaluation index is constructed, and the correlation judgment numerical relationship between the evaluation indexes is constructed. The numerical relationship can be expressed as
(16)k=1N∑j=1NFjA,where k represents the calculated correlation parameter, Fj represents the index data set after assignment, and A represents the calibration parameter.


## 4.2. Analysis of Experimental Results

According to the correlation value of the indicators calculated above, after repeated iterative calculation for 10 times, sort out the calculated correlation value. Finally, the correlation between the participating evaluation indicators in the three evaluation methods is shown in Figure3.Figure 3
Correlation results of three effect evaluation methods.Corresponding to the constructed correlation value judgment results, when the defined correlation value is infinitely close to 1, it indicates that the indicators selected by this evaluation method are highly correlated. After ten iterations, according to the data points calculated and distributed in Figure3, the correlation parameters calculated in reference [4] are between -2 and 2, and the correlation of the indicators involved in the evaluation process is poor. The correlation value calculated by the method in reference [5] is between -2.5 and 0.8, and the correlation of the selected indicators in the evaluation process is poor. The correlation index calculated in the iterative process of the designed evaluation method tends to be close to the value 1. Compared with the two existing evaluation methods, the correlation index selected in the evaluation process is the strongest.Under the above experimental environment, corresponding to the index parameters of the above assignment processing, calculate theF statistics formed by the index parameters. The numerical relationship of the F statistics can be expressed as
(17)TF=sμ+V,where TF represents the calculated F statistic, μ represents the data test value, V represents the statistic of the parameter, and s represents the score difference parameter. Corresponding to the numerical relationship calculated above, sort out the F statistics calculated by three evaluation methods. The numerical relationship of statistics is shown in Figure 4.Figure 4
Numerical results ofF statistics calculated by three evaluation methods.Under the numerical relationship constructed in Figure4, the data set prepared for the experiment is repeatedly calculated. When the value of F statistic is close to 0.5, it shows that the automatic evaluation method shows strong significance, and the results of this evaluation method are highly scientific. According to the numerical relationship of statistical arrangement, the value of F statistic calculated in reference [4] is between 0.9 and 1.0, and the significance of this evaluation method is poor. The value of F statistic obtained by the method in reference [5] is about 0.2~0.4, and this evaluation method fails to show strong significance. The F statistic calculated by the designed evaluation method is about 0.5. Compared with the applied evaluation method, the evaluation result of this evaluation process is the most scientific.Keep the above experimental environment unchanged, call the index parameters of the above assignment processing, and use the associated Legendre function to construct the evaluation accuracy of the evaluation method. The accuracy value can be expressed as(18)χn=costPid,τn=χnα,where Pi represents the associated Legendre function, χn represents the infinite order function, cost represents the orthogonal curve, d represents the class II parameter of the index, α represents the dimensional scale parameter, and τn represents the value of evaluation accuracy. According to the accuracy numerical relationship of the above structure, sort out the calculated values. The evaluation accuracy results of the three evaluation methods are shown in Figure 5.Figure 5
Accuracy values calculated by three evaluation methods.Corresponding to the precision value relationship calculated in Figure5, the closer the known defined precision value is to 1, the better the precision of this evaluation method. According to the accuracy numerical results shown in Figure 5, the accuracy value in document [4] is 0.4, and the accuracy of the evaluation result is the worst. The accuracy value calculated by the method in reference [5] is 0.6, and the accuracy of the evaluation result is better. The precision calculated by the designed evaluation method is 0.8. Compared with the two selected comparison methods, the designed evaluation method has the best accuracy.The dynamic comparison results of the three tourism-driven real estate economic growth quality joint evaluation models are shown in Figure6.Figure 6
Dynamic comparison results of different models.It can be seen from the results in Figure6 that the economic evaluation method of tourism-driven real estate based on AHP designed in this paper is highly consistent with the time curve, because the evaluation model is based on the possibility index, severity index, and growth quality index of the growth quality of tourism-driven real estate user data before evaluating the economic growth quality of tourism-driven real estate. By screening and ranking the growth quality, this paper effectively analyzes the quality of tourism-driven real estate economic growth, which makes the dynamic degree of the evaluation model better.There is a close relationship between the evaluation results and the reliability of the evaluation results. The more accurate the evaluation results are, the more reliable the evaluation results are. Figure7 shows the comparison results of the reliability of the evaluation results of three different methods.Figure 7
Comparison of reliability of evaluation results of different methods.By analyzing the experimental data in Figure7, it can be seen that the proposed method has higher evaluation accuracy, so the reliability of the evaluation results of the whole method is better. At the same time, the proposed method also focuses on the analysis of the relevant influencing factors of the economic growth quality of tourism-driven real estate and defines the level, and the whole operation process is completed by a computer. It has high reliability. In the actual evaluation process of the other two methods, all the scores are artificially formulated, so the overall reliability is lower.Test the three methods to extract and classify the evaluation indexes of tourism-driven real estate economic growth quality and compare and analyze the classification performance of the evaluation indexes of each method by using the PR curve. The experimental results are shown in Figure8.Figure 8
PR curves of different methods.According to the analysis of Figure8, when the three methods are used to classify the evaluation indexes of tourism-driven real estate economic growth quality, respectively, with the gradual increase of accuracy, the recall index gradually decreases. The maximum accuracy of the reference [4] method is 82%, the maximum accuracy of the reference [5] method can reach 88%, and the maximum accuracy of this method can reach 96%. The results show that the tourism-driven real estate economic growth quality evaluation index of this method has the best classification and recognition ability.


## 5. Conclusion

Aiming at the statistical evaluation of tourism-driven real estate economic growth, a multi-index statistical evaluation method based on an analytic hierarchy process is designed. The research shows that this method can solve the problems existing in the previous statistical evaluation of tourism-driven real estate economic growth. Build the statistical evaluation model of tourism-driven real estate economic growth, conduct quantitative analysis of statistical evaluation indicators of tourism-driven real estate economic growth, and improve the accuracy of statistical evaluation of tourism-driven real estate economic growth. The experimental test results show that the correlation index of the tourism-driven real estate economic growth quality evaluation method based on AHP tends to be close to the value 1, the value ofF statistic is about 0.5, and the accuracy value is 0.8 in the iterative process, which proves that this method has higher relevance, scientificity, and accuracy for the mining of the tourism-driven real estate economic growth quality evaluation index. At the same time, the consistency between the evaluation method proposed in this paper and the time curve is higher and the reliability is better, which further verifies the effectiveness of the proposed method. In the later development, the application of this design method in the statistical evaluation of tourism-driven real estate economic growth should be strengthened. In the future, research is also necessary to further optimize the statistical evaluation method of tourism-driven real estate economic growth, so as to provide a reference for the accuracy and efficiency of statistical evaluation of tourism-driven real estate economic growth in various regions.



---

*Source: 7252544-2022-03-11.xml*