# Evaluation of High-quality Development of Shaanxi’s Economy Based on Digital Economy Based on Machine Learning Algorithm

Authors: Lina Wang
Journal: International Transactions on Electrical Energy Systems
 (2022)

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## Abstract

With the strong development of industrial transformation, the digital economy, as the most dynamic, innovative, and widely radiated economic form at present, has become an important force driving the development of the global economy. Shaanxi Province has accelerated the development of the digital economy, and the development of the province’s digital economy has shown a good trend. However, the current digital economy development measurement is still unable to accurately observe the development law of the digital economy industry, which makes it difficult to measure the control standards for the digital economy industry. Therefore, this study has evaluated the development of digital economy in Shaanxi Province based on machine learning algorithms. This study first puts forward the dimensions of digital economy monitoring and evaluation and then combines the characteristics of digital economy development in Shaanxi Province to determine the quality evaluation indicators of digital economy development. Finally, according to the machine learning algorithm, a quality evaluation model of digital economy development was established, and through the evaluation results, suggestions were put forward for the development plan of digital economy in Shaanxi Province. The experimental results showed that, after re-planning the digital economy in Shaanxi Province according to the evaluation results, the development of the digital economy in Shaanxi Province has increased by 9.82%, indicating that the evaluation model plays a good reference role in the formulation of the digital economy development plan.


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## Body

## 1. Introduction

Shaanxi Province has made great progress in the fields of digital economy such as online shopping, online entertainment, mobile communication, and online education, which has played the dual function of promoting epidemic prevention and control and economic and social development. In the process of promoting the scale of the digital economy, the main tasks of Shaanxi Province are as follows. The first is information infrastructure construction. Shaanxi has implemented communications’ infrastructure construction actions for four consecutive years, which comprehensively enhances the supporting capacity of information infrastructure. The second is to innovate and promote the pilot demonstration of the digital economy, accelerating the implementation of special actions such as “Internet +” and “Intelligence +.” The third is to implement the “five major projects” for poverty alleviation through the Internet, and the e-commerce poverty alleviation and consumption poverty alleviation have achieved remarkable results. The fourth is to carry out international exchanges and cooperation in information infrastructure construction and digital economy development. However, there is currently no method to accurately assess the development level of the digital economy, which is an important issue that remains to be resolved.Many scholars have conducted research on the digital economy. Han and Ding studied the impact of the digital economy on total factor carbon productivity [1]. In the context of COVID-19, Li and Liang examined the impact of the digital economy on the integration of China’s cultural tourism industry [2]. Wang et al. discussed the role of the digital economy in green innovation and put forward relevant suggestions for improving geographic information capabilities [3]. Chizhikov et al. considered a theoretical approach that made it possible to formulate rational choices for the construction of distributed systems and telecommunications in the digital economy under the influence of external special software and hardware [4]. On the basis of measuring the level of data economy at the city level, combined with pm2.5 data of haze pollution and related economic statistics, Huang discussed the relationship between the development level of digital economy and urban haze pollution from both theoretical and empirical levels [5]. Although there are many studies on the digital economy, further research is needed on the digital economy to promote the high-quality development of Shaanxi’s economy.Machine learning algorithms are widely used in evaluation. Frolov et al. used machine learning to assess changes in cerebral cortical synchrony under long-term cognitive load [6]. Beswick et al. evaluated the improvement of sinus computed tomography with CFTR modulator treatment by machine learning [7]. Hung used machine learning to evaluate the performance and predicted outcomes of robotic-assisted radical prostatectomy [8]. Guimaraes et al. evaluated whether the use of a machine learning software program can accurately define the use of a standardized reporting template and language for non-compliance with a human review performed by a machine learning software program [9]. Trentzsch and Schumann used a machine learning algorithm to identify gait parameters suitable for assessing subtle changes in gait in patients with multiple sclerosis [10]. Although machine learning algorithms are often used for evaluation, there is little research on machine learning algorithms in the evaluation of high-quality economic development.This study first describes the dimensions of digital economy monitoring and evaluation, including six parts: digital infrastructure, data resource elements, digital technology innovation, digital industry development, digital industry application, and digital social governance. Then, based on these six dimensions, the quality evaluation index of digital economy development is proposed. Then, Support Vector Machine (SVM),K-Nearest Neighbor Algorithm (KNN), K-Means Clustering, and Gaussian Mixture Model are selected as models for evaluating the quality of digital economy development. Finally, according to the evaluation results, suggestions are put forward for the development of the digital economy in Shaanxi Province, and the digital development of Shaanxi Province is re-evaluated after adjustment according to the suggestions.


## 2. Evaluation Model of High-Quality Development of Digital Economy Based on Machine Learning Algorithm

### 2.1. Dimensions of Digital Economy Monitoring and Evaluation Based on High-Quality Development

The role of digital technology in the economy and society is mainly reflected in two aspects. One is the diffusion process of digital infrastructure, and the other is the integration process of digital technology and social environment [11]. Based on the above framework, this study divides the evaluation dimension of the digital economy into two parts. The theoretical framework of digital economy monitoring is shown in Figure 1.Figure 1
Theoretical framework of digital economy monitoring.The base layer of the digital economy includes three parts: digital infrastructure, data resource elements, and digital technology innovation. The digital economy contribution layer includes three parts: digital industry development, digital industry application and digital social governance. The development and application of the digital industry reflect the economic benefits of the digital economy, and the governance of the digital society reflects the social benefits of the digital economy.


### 2.2. Selection of Quality Evaluation Indicators for Digital Economy Development

Combined with the construction goals and development characteristics of digital economy cities in Shaanxi Province, on the basis of six first-level dimensions, two-level dimensions and three-level indicators are determined [12]. The specific indicators are shown in Table 1.Table 1
Digital economy development quality indicators.
First-level indicatorsTwo-level indicatorsThree-level indicatorsDigital infrastructureInfrastructureNumber of 5 G base stations builtThe proportion of new infrastructure in the cityCity’s per capital computing powerOperational levelNumber of mobile subscribers per 100 inhabitants5 G end user penetration rateGigabit broadband household penetrationEnvironmental basicsEase of doing businessData resource elementsOpen sharingPublic data open indexData transactionBig data transaction volume growthDigital technology innovationInnovation inputResearch and development (R&D) investment intensity of digital economy enterprisesInnovation environmentGlobal science and technology innovation center indexCreativityGrowth rate and proportion of graduates majoring in digital economyThe proportion of ICT employees in the city’s employeesR&D personnel account for the proportion of employees in the cityInnovative achievementsIncome and growth rate of enterprises in independent innovation demonstration zoneThe number of papers published in top journals in the field of computer scienceThe number and proportion of digital economy invention patents authorizedDigital industry developmentScale structureThe added value of the core industries of the digital economy accounts for the proportion of the cityThe proportion of e-commerce transaction volume in the national e-commerce transaction volumeDevelopment potentialThe proportion of the top 100 digital economy benchmarking companies in the world by market capitalizationThe number of digital economy unicorn companies accounts for the proportion of the worldThe scale of financing of digital economy public enterprisesOpen to the outside worldDigitizable trade in servicesThe scale of investment in other countries in the digital economyThe scale of capital utilization of other countries in the digital economyDigital industry applicationDigital manufacturingGrowth rate of total output value of digital manufacturing enterprisesTechnology contract turnover and growth rate of digital manufacturing enterprisesDigital mobilityNumber of intelligent and connected roads builtAutonomous driving road milesAverage daily service person-time of public transport mobile paymentDynamic data access rate of operating record parking lotMonthly active users of key internet travel platformsDigital healthMonthly active users of key internet medical platformsThe coverage rate of personal health records for each citizen, one codeDigital educationThe number of people studying online on the internet education platformDigital financeGrowth rate of third-party mobile payment amountOperating income of key financial technology companiesOnline payment inter-bank clearing system business volumeDigital society governanceDigital governmentThe rate of online handling of government service mattersThe ratio of actual handling of government service mattersMonthly active users of online government servicesTotal applications of electronic business licensesDigital lifeDigital life citizen satisfactionIntelligent rate of end systems of water, electricity, and gas systems


### 2.3. Construction of the Evaluation Model

This research collected the analysis index data from 2015 to 2021 and selectedK-Means clustering and Gaussian mixture model as the model to evaluate the quality of digital economy development.


#### 2.3.1. Support Vector Machine Evaluation Model

In machine learning, SVM is a widely used supervised learning algorithm, which is commonly used for pattern recognition, classification, and regression analysis [13]. The SVM is shown in Figure 2.Figure 2
Support vector machine.SVM is used for classification in the research work of this study. The constraint of SVM is as follows:(1)xiwTyi+b≥1.When considering the problem of outliers, the constraints become formulas (2) and (3):(2)xiwTyi+b≥1−ξi,(3)ξi≥0.Ifξi satisfies formula (3), it is called a slack variable.Constraints are added so that the sum ofξi is minimized:(4)min12w2+C∑i=1nξi,where C is a parameter.For nonlinear problems, a nonlinear SVM optimization problem can be obtained by constructing a Lagrangian function and solving the derivative in some way:(5)min12∑i=1N∑j=1NαiαjβiβjHyi,yj−∑i=1Nαi,∑i=1Nαiβi=0,0≤αi≤C.When faced with data samples from low-level regions that cannot be divided, kernel functions can be used to map data samples from low-level regions to high-level regions to solve the problem. The commonly used kernel functions are as follows.(a)
Linear kernel function:(6)Hyi,yj=yi,yj.Linear kernel function classification results are good. Therefore, when processing data, the first attempt is to use the linear kernel function to classify.(b)
Polynomial kernel function:(7)Hyi,yj=yi,yj+1d.The polynomial kernel function has many parameters. When the order is high, the computational complexity is too large to calculate.(c)
Gaussian radial basis kernel function:(8)Hyi,yj=exp−γyi−yj2,γ=12δ2.The Gaussian radial basis function has better performance and less parameters.(d)
Sigmoid kernel function:(9)Hyi,yj=tanhHyi,yj−δ.In this study, in order to select the best clustering result, the radial basis kernel function is used for training, and then, it is validated on the training dataset to find the best C and y parameters. Finding better C and y enables the classifier to correctly predict the test set data, while making the classification accuracy relatively high [14].


#### 2.3.2.K-Nearest Neighbor Algorithm

TheK-nearest neighbor algorithm is shown in Figure 3.Figure 3
K-nearest neighbor algorithm.Figure3 shows which group the test sample in the image, the yellow five-pointed star, should belong to. If the value of K is set to 3, which is the extent of the blue circle, then the yellow star is assigned to the green triangle group because there are two green triangles inside the circle and only one blue square. If the value of K is set to 5, which is the range of the red line circle, then the yellow five-pointed star is assigned to the blue square group.In the field of pattern recognition technology, KNN algorithm is widely used for classification. In this study, the classification technique is mainly applied to the nearest neighbor algorithm.When calculating the distance between objects in KNN, in order to avoid matching problems between objects, Euclidean distance or Manhattan distance is often used [15].Euclidean distance:(10)dx,y=∑k=1nxk−yk2.Manhattan distance:(11)dx,y=∑k=1nxk−yk.In the nearest neighbor, the value ofK affects the clustering results. If the K value is relatively small, overfitting easily occurs. If the K value is relatively large, it increases the training error, so the optimal K value can be selected by the method of cross-validation [16].The training process of theK-nearest neighbor prediction model is as follows.Input data:M is the dataset.Training process:M is divided into two parts, training set and test set. New validation data are added to the training dataset circularly. The distance between the new data and each training sample is calculated. The K samples closest to the new data are found. The number of occurrences of each class in the K samples is counted. The class with the highest frequency is selected as the class of the new data.Output: it is the accuracy of theK-nearest neighbor prediction model on the test set.


#### 2.3.3.K-Means Clustering

TheK-Means has good measurement performance in a large number of samples and is widely used in many different fields [17].Given a set of observations,x = {x, x2...x.}, the goal of K-Means is to obtain(12)argmin∑i=1k∑x∈six−μi2,where μi is the mean of the midpoints of si.The training process of theK-Means clustering prediction model is as follows [18].Input data: tThe raw datasetMX1,X2,…,Xn that is unlabeled is input.Training processK cluster centroid points are randomly selected. The variance of the remaining data at the K centroids is calculated separately, and these data are input into the cluster with the smallest variance. The different centers of the K groups are recalculated based on the clustering results by taking the arithmetic mean of the different measurements of all elements in the group. All points in M are regrouped according to the new center. The fourth step is repeated until the clustering results no longer change.Output: iIt is the prediction accuracy of theK-Means clustering model.


#### 2.3.4. Gaussian Mixture Model

The Gaussian mixture model algorithm process is as follows [19].The numberM of Gaussian distributions in the Gaussian mixture model is set, and the parameter θ in the Gaussian mixture model is initialized.θ is the set of mean function μ, covariance matrix Σ, and prior probability aj for each model scale.Pj|x;θ is calculated:(13)Pj|x;θ=ajNjxi;θ∑lMalNlxi;θ,Nix=12πdΣiexp−12x−μit∑i−1x−μi.The weights are updated:(14)aj=Σi=1nPijN.The mean is undated:(15)μ=Σi=1nPijxjΣi=1nPij.The covariance matrix is updated:(16)∑j=Σi=1nPijxi−μjxi−μjTΣi=1nPij.The above steps are repeated until the convergence condition is satisfied [20].The training process for a Gaussian mixture model is as follows.Input data: the raw dataset 1 without labels is input.Training process: for each Gaussian distribution, its mean and random variance are given. For each data sample, its probability in a Gaussian distribution is calculated. For each Gaussian distribution, the contribution of the Gaussian distribution for each data sample based on the probability is calculated [21, 22]. If the probability is large, the contribution is large, and if the probability is small, the contribution is small. Therefore, the contribution of the data samples to the Gaussian distribution is used as the weight to calculate the weighted mean and variance. The steps are repeated until both the mean and variance of the Gaussian distribution converge.Output: it is the prediction accuracy of the Gaussian mixture model.


## 2.1. Dimensions of Digital Economy Monitoring and Evaluation Based on High-Quality Development

The role of digital technology in the economy and society is mainly reflected in two aspects. One is the diffusion process of digital infrastructure, and the other is the integration process of digital technology and social environment [11]. Based on the above framework, this study divides the evaluation dimension of the digital economy into two parts. The theoretical framework of digital economy monitoring is shown in Figure 1.Figure 1
Theoretical framework of digital economy monitoring.The base layer of the digital economy includes three parts: digital infrastructure, data resource elements, and digital technology innovation. The digital economy contribution layer includes three parts: digital industry development, digital industry application and digital social governance. The development and application of the digital industry reflect the economic benefits of the digital economy, and the governance of the digital society reflects the social benefits of the digital economy.


## 2.2. Selection of Quality Evaluation Indicators for Digital Economy Development

Combined with the construction goals and development characteristics of digital economy cities in Shaanxi Province, on the basis of six first-level dimensions, two-level dimensions and three-level indicators are determined [12]. The specific indicators are shown in Table 1.Table 1
Digital economy development quality indicators.
First-level indicatorsTwo-level indicatorsThree-level indicatorsDigital infrastructureInfrastructureNumber of 5 G base stations builtThe proportion of new infrastructure in the cityCity’s per capital computing powerOperational levelNumber of mobile subscribers per 100 inhabitants5 G end user penetration rateGigabit broadband household penetrationEnvironmental basicsEase of doing businessData resource elementsOpen sharingPublic data open indexData transactionBig data transaction volume growthDigital technology innovationInnovation inputResearch and development (R&D) investment intensity of digital economy enterprisesInnovation environmentGlobal science and technology innovation center indexCreativityGrowth rate and proportion of graduates majoring in digital economyThe proportion of ICT employees in the city’s employeesR&D personnel account for the proportion of employees in the cityInnovative achievementsIncome and growth rate of enterprises in independent innovation demonstration zoneThe number of papers published in top journals in the field of computer scienceThe number and proportion of digital economy invention patents authorizedDigital industry developmentScale structureThe added value of the core industries of the digital economy accounts for the proportion of the cityThe proportion of e-commerce transaction volume in the national e-commerce transaction volumeDevelopment potentialThe proportion of the top 100 digital economy benchmarking companies in the world by market capitalizationThe number of digital economy unicorn companies accounts for the proportion of the worldThe scale of financing of digital economy public enterprisesOpen to the outside worldDigitizable trade in servicesThe scale of investment in other countries in the digital economyThe scale of capital utilization of other countries in the digital economyDigital industry applicationDigital manufacturingGrowth rate of total output value of digital manufacturing enterprisesTechnology contract turnover and growth rate of digital manufacturing enterprisesDigital mobilityNumber of intelligent and connected roads builtAutonomous driving road milesAverage daily service person-time of public transport mobile paymentDynamic data access rate of operating record parking lotMonthly active users of key internet travel platformsDigital healthMonthly active users of key internet medical platformsThe coverage rate of personal health records for each citizen, one codeDigital educationThe number of people studying online on the internet education platformDigital financeGrowth rate of third-party mobile payment amountOperating income of key financial technology companiesOnline payment inter-bank clearing system business volumeDigital society governanceDigital governmentThe rate of online handling of government service mattersThe ratio of actual handling of government service mattersMonthly active users of online government servicesTotal applications of electronic business licensesDigital lifeDigital life citizen satisfactionIntelligent rate of end systems of water, electricity, and gas systems


## 2.3. Construction of the Evaluation Model

This research collected the analysis index data from 2015 to 2021 and selectedK-Means clustering and Gaussian mixture model as the model to evaluate the quality of digital economy development.


### 2.3.1. Support Vector Machine Evaluation Model

In machine learning, SVM is a widely used supervised learning algorithm, which is commonly used for pattern recognition, classification, and regression analysis [13]. The SVM is shown in Figure 2.Figure 2
Support vector machine.SVM is used for classification in the research work of this study. The constraint of SVM is as follows:(1)xiwTyi+b≥1.When considering the problem of outliers, the constraints become formulas (2) and (3):(2)xiwTyi+b≥1−ξi,(3)ξi≥0.Ifξi satisfies formula (3), it is called a slack variable.Constraints are added so that the sum ofξi is minimized:(4)min12w2+C∑i=1nξi,where C is a parameter.For nonlinear problems, a nonlinear SVM optimization problem can be obtained by constructing a Lagrangian function and solving the derivative in some way:(5)min12∑i=1N∑j=1NαiαjβiβjHyi,yj−∑i=1Nαi,∑i=1Nαiβi=0,0≤αi≤C.When faced with data samples from low-level regions that cannot be divided, kernel functions can be used to map data samples from low-level regions to high-level regions to solve the problem. The commonly used kernel functions are as follows.(a)
Linear kernel function:(6)Hyi,yj=yi,yj.Linear kernel function classification results are good. Therefore, when processing data, the first attempt is to use the linear kernel function to classify.(b)
Polynomial kernel function:(7)Hyi,yj=yi,yj+1d.The polynomial kernel function has many parameters. When the order is high, the computational complexity is too large to calculate.(c)
Gaussian radial basis kernel function:(8)Hyi,yj=exp−γyi−yj2,γ=12δ2.The Gaussian radial basis function has better performance and less parameters.(d)
Sigmoid kernel function:(9)Hyi,yj=tanhHyi,yj−δ.In this study, in order to select the best clustering result, the radial basis kernel function is used for training, and then, it is validated on the training dataset to find the best C and y parameters. Finding better C and y enables the classifier to correctly predict the test set data, while making the classification accuracy relatively high [14].


### 2.3.2.K-Nearest Neighbor Algorithm

TheK-nearest neighbor algorithm is shown in Figure 3.Figure 3
K-nearest neighbor algorithm.Figure3 shows which group the test sample in the image, the yellow five-pointed star, should belong to. If the value of K is set to 3, which is the extent of the blue circle, then the yellow star is assigned to the green triangle group because there are two green triangles inside the circle and only one blue square. If the value of K is set to 5, which is the range of the red line circle, then the yellow five-pointed star is assigned to the blue square group.In the field of pattern recognition technology, KNN algorithm is widely used for classification. In this study, the classification technique is mainly applied to the nearest neighbor algorithm.When calculating the distance between objects in KNN, in order to avoid matching problems between objects, Euclidean distance or Manhattan distance is often used [15].Euclidean distance:(10)dx,y=∑k=1nxk−yk2.Manhattan distance:(11)dx,y=∑k=1nxk−yk.In the nearest neighbor, the value ofK affects the clustering results. If the K value is relatively small, overfitting easily occurs. If the K value is relatively large, it increases the training error, so the optimal K value can be selected by the method of cross-validation [16].The training process of theK-nearest neighbor prediction model is as follows.Input data:M is the dataset.Training process:M is divided into two parts, training set and test set. New validation data are added to the training dataset circularly. The distance between the new data and each training sample is calculated. The K samples closest to the new data are found. The number of occurrences of each class in the K samples is counted. The class with the highest frequency is selected as the class of the new data.Output: it is the accuracy of theK-nearest neighbor prediction model on the test set.


### 2.3.3.K-Means Clustering

TheK-Means has good measurement performance in a large number of samples and is widely used in many different fields [17].Given a set of observations,x = {x, x2...x.}, the goal of K-Means is to obtain(12)argmin∑i=1k∑x∈six−μi2,where μi is the mean of the midpoints of si.The training process of theK-Means clustering prediction model is as follows [18].Input data: tThe raw datasetMX1,X2,…,Xn that is unlabeled is input.Training processK cluster centroid points are randomly selected. The variance of the remaining data at the K centroids is calculated separately, and these data are input into the cluster with the smallest variance. The different centers of the K groups are recalculated based on the clustering results by taking the arithmetic mean of the different measurements of all elements in the group. All points in M are regrouped according to the new center. The fourth step is repeated until the clustering results no longer change.Output: iIt is the prediction accuracy of theK-Means clustering model.


### 2.3.4. Gaussian Mixture Model

The Gaussian mixture model algorithm process is as follows [19].The numberM of Gaussian distributions in the Gaussian mixture model is set, and the parameter θ in the Gaussian mixture model is initialized.θ is the set of mean function μ, covariance matrix Σ, and prior probability aj for each model scale.Pj|x;θ is calculated:(13)Pj|x;θ=ajNjxi;θ∑lMalNlxi;θ,Nix=12πdΣiexp−12x−μit∑i−1x−μi.The weights are updated:(14)aj=Σi=1nPijN.The mean is undated:(15)μ=Σi=1nPijxjΣi=1nPij.The covariance matrix is updated:(16)∑j=Σi=1nPijxi−μjxi−μjTΣi=1nPij.The above steps are repeated until the convergence condition is satisfied [20].The training process for a Gaussian mixture model is as follows.Input data: the raw dataset 1 without labels is input.Training process: for each Gaussian distribution, its mean and random variance are given. For each data sample, its probability in a Gaussian distribution is calculated. For each Gaussian distribution, the contribution of the Gaussian distribution for each data sample based on the probability is calculated [21, 22]. If the probability is large, the contribution is large, and if the probability is small, the contribution is small. Therefore, the contribution of the data samples to the Gaussian distribution is used as the weight to calculate the weighted mean and variance. The steps are repeated until both the mean and variance of the Gaussian distribution converge.Output: it is the prediction accuracy of the Gaussian mixture model.


## 2.3.1. Support Vector Machine Evaluation Model

In machine learning, SVM is a widely used supervised learning algorithm, which is commonly used for pattern recognition, classification, and regression analysis [13]. The SVM is shown in Figure 2.Figure 2
Support vector machine.SVM is used for classification in the research work of this study. The constraint of SVM is as follows:(1)xiwTyi+b≥1.When considering the problem of outliers, the constraints become formulas (2) and (3):(2)xiwTyi+b≥1−ξi,(3)ξi≥0.Ifξi satisfies formula (3), it is called a slack variable.Constraints are added so that the sum ofξi is minimized:(4)min12w2+C∑i=1nξi,where C is a parameter.For nonlinear problems, a nonlinear SVM optimization problem can be obtained by constructing a Lagrangian function and solving the derivative in some way:(5)min12∑i=1N∑j=1NαiαjβiβjHyi,yj−∑i=1Nαi,∑i=1Nαiβi=0,0≤αi≤C.When faced with data samples from low-level regions that cannot be divided, kernel functions can be used to map data samples from low-level regions to high-level regions to solve the problem. The commonly used kernel functions are as follows.(a)
Linear kernel function:(6)Hyi,yj=yi,yj.Linear kernel function classification results are good. Therefore, when processing data, the first attempt is to use the linear kernel function to classify.(b)
Polynomial kernel function:(7)Hyi,yj=yi,yj+1d.The polynomial kernel function has many parameters. When the order is high, the computational complexity is too large to calculate.(c)
Gaussian radial basis kernel function:(8)Hyi,yj=exp−γyi−yj2,γ=12δ2.The Gaussian radial basis function has better performance and less parameters.(d)
Sigmoid kernel function:(9)Hyi,yj=tanhHyi,yj−δ.In this study, in order to select the best clustering result, the radial basis kernel function is used for training, and then, it is validated on the training dataset to find the best C and y parameters. Finding better C and y enables the classifier to correctly predict the test set data, while making the classification accuracy relatively high [14].


## 2.3.2.K-Nearest Neighbor Algorithm

TheK-nearest neighbor algorithm is shown in Figure 3.Figure 3
K-nearest neighbor algorithm.Figure3 shows which group the test sample in the image, the yellow five-pointed star, should belong to. If the value of K is set to 3, which is the extent of the blue circle, then the yellow star is assigned to the green triangle group because there are two green triangles inside the circle and only one blue square. If the value of K is set to 5, which is the range of the red line circle, then the yellow five-pointed star is assigned to the blue square group.In the field of pattern recognition technology, KNN algorithm is widely used for classification. In this study, the classification technique is mainly applied to the nearest neighbor algorithm.When calculating the distance between objects in KNN, in order to avoid matching problems between objects, Euclidean distance or Manhattan distance is often used [15].Euclidean distance:(10)dx,y=∑k=1nxk−yk2.Manhattan distance:(11)dx,y=∑k=1nxk−yk.In the nearest neighbor, the value ofK affects the clustering results. If the K value is relatively small, overfitting easily occurs. If the K value is relatively large, it increases the training error, so the optimal K value can be selected by the method of cross-validation [16].The training process of theK-nearest neighbor prediction model is as follows.Input data:M is the dataset.Training process:M is divided into two parts, training set and test set. New validation data are added to the training dataset circularly. The distance between the new data and each training sample is calculated. The K samples closest to the new data are found. The number of occurrences of each class in the K samples is counted. The class with the highest frequency is selected as the class of the new data.Output: it is the accuracy of theK-nearest neighbor prediction model on the test set.


## 2.3.3.K-Means Clustering

TheK-Means has good measurement performance in a large number of samples and is widely used in many different fields [17].Given a set of observations,x = {x, x2...x.}, the goal of K-Means is to obtain(12)argmin∑i=1k∑x∈six−μi2,where μi is the mean of the midpoints of si.The training process of theK-Means clustering prediction model is as follows [18].Input data: tThe raw datasetMX1,X2,…,Xn that is unlabeled is input.Training processK cluster centroid points are randomly selected. The variance of the remaining data at the K centroids is calculated separately, and these data are input into the cluster with the smallest variance. The different centers of the K groups are recalculated based on the clustering results by taking the arithmetic mean of the different measurements of all elements in the group. All points in M are regrouped according to the new center. The fourth step is repeated until the clustering results no longer change.Output: iIt is the prediction accuracy of theK-Means clustering model.


## 2.3.4. Gaussian Mixture Model

The Gaussian mixture model algorithm process is as follows [19].The numberM of Gaussian distributions in the Gaussian mixture model is set, and the parameter θ in the Gaussian mixture model is initialized.θ is the set of mean function μ, covariance matrix Σ, and prior probability aj for each model scale.Pj|x;θ is calculated:(13)Pj|x;θ=ajNjxi;θ∑lMalNlxi;θ,Nix=12πdΣiexp−12x−μit∑i−1x−μi.The weights are updated:(14)aj=Σi=1nPijN.The mean is undated:(15)μ=Σi=1nPijxjΣi=1nPij.The covariance matrix is updated:(16)∑j=Σi=1nPijxi−μjxi−μjTΣi=1nPij.The above steps are repeated until the convergence condition is satisfied [20].The training process for a Gaussian mixture model is as follows.Input data: the raw dataset 1 without labels is input.Training process: for each Gaussian distribution, its mean and random variance are given. For each data sample, its probability in a Gaussian distribution is calculated. For each Gaussian distribution, the contribution of the Gaussian distribution for each data sample based on the probability is calculated [21, 22]. If the probability is large, the contribution is large, and if the probability is small, the contribution is small. Therefore, the contribution of the data samples to the Gaussian distribution is used as the weight to calculate the weighted mean and variance. The steps are repeated until both the mean and variance of the Gaussian distribution converge.Output: it is the prediction accuracy of the Gaussian mixture model.


## 3. Experiment Results and Discussion of Digital Economy Assessment

The experiment collected data on digital economy indicators in Shaanxi Province from 2015 to 2021. The evaluation of digital economy development was carried out from six aspects: digital infrastructure, digital resource elements, digital technology innovation, digital industry development, digital industry application, and digital social governance [22, 23]. According to the evaluation results, suggestions have been put forward for the development plan of the digital economy in Shaanxi Province, and the development status of the digital economy in Shaanxi Province was re-evaluated according to the data after the plan [24].


### 3.1. Digital Infrastructure

The digital infrastructure index value of Shaanxi Province in 2015 is set to 100, and the evaluation results of digital infrastructure from 2015 to 2021 are shown in Figure4.Figure 4
Digital infrastructure evaluation results.The evaluation results of digital infrastructure in Shaanxi Province in 2016 and 2017 were relatively close to those in 2015, and the development of digital infrastructure in these two years has not changed much. Since 2018, Shaanxi Province has increased its investment in digital infrastructure, and the evaluation results of digital infrastructure have improved year by year. Especially in 2020 and 2021, the evaluation results of digital infrastructure have increased significantly. The evaluation result in 2019 was 117.83, which was raised to 132.58 in 2020 and 146.69 in 2021. It can be seen that the development of digital infrastructure in Shaanxi Province has made great progress in the past two years. In order to further improve the construction of digital infrastructure, it is necessary to integrate the network equipment of the four major operators, including Telecom, China Mobile, China Unicom, and Radio and Television, increasing investment in various fields of “new infrastructure” and accelerating the pace of construction of new infrastructure such as data centers and 5 G networks, to improve network equipment and implement speed-up and fee reduction.


### 3.2. Elements of Digital Resources

Setting the index value of digital resource elements in Shaanxi Province in 2015 to 100, the evaluation results of digital resource elements from 2015 to 2021 are shown in Figure5.Figure 5
Evaluation results of digital resource elements.The development of digital resource elements in Shaanxi Province was relatively slow in 2016 and 2017, but has been greatly improved in 2018. It increased from 108.69 in 2017 to 119.74 and then maintained a relatively fast growth rate year after year. In 2021, the evaluation result of digital resource elements was improved to 136.84. To further enhance the construction of digital resource elements, it is necessary to accelerate the construction of an intelligent society, with the interconnection of different operating systems as the main line, and to promote the co-construction, sharing of software and hardware facilities.


### 3.3. Digital Technology Innovation

The digital technology innovation index value of Shaanxi Province in 2015 was set to 100, and the evaluation results of digital technology innovation from 2015 to 2021 are shown in Figure6.Figure 6
Digital technology innovation evaluation results.The value of the digital technology innovation index in Shaanxi Province showed a gradually increasing trend, and its growth rate was the fastest in 2019 and 2020. The index value of digital technology innovation in 2019 was 116.69. The index value in 2020 was raised to 123.45, and it was 127.85 in 2021. To further enhance the construction of digital technology innovation, it is necessary to further increase the research and development investment intensity of digital economy enterprises and introduce digital economy professionals.


### 3.4. Development of Digital Industry

The digital industry development index value of Shaanxi Province in 2015 was set to 100, and the evaluation results of digital industry development from 2015 to 2021 are shown in Figure7.Figure 7
Digital industry development evaluation results.The development speed of the digital industry was relatively slow, showing a steady upward trend as a whole. In 2021, the development index value of the digital industry in Shaanxi Province was 157.36, and the development trend was good. To further enhance the construction of digital industry development, it is necessary to integrate good industries such as electronic information industry and software service industry to improve the effect of industrial integration. At the same time, relying on the existing industrial foundation and scientific and technological research and development advantages, the construction of digital industrial parks and bases needs to be continuously strengthened, and the radiation effect of the park should be enhanced.


### 3.5. Digital Industry Applications

The digital industry application index value of Shaanxi Province in 2015 was set to 100, and the evaluation results of digital industry application in 2015–2021 are shown in Figure8.Figure 8
Digital industry application evaluation results.In terms of digital industry applications, the development of digital industry applications was relatively slow from 2015 to 2018. It improved in 2019 and significantly improved in 2020 and 2021. The value of the digital industry application index in 2020 was 137.35, and the index value in 2021 was 174.93. To further enhance the construction of digital industry development, it is necessary to break industrial boundaries to promote industrial integration and industrial model innovation, forming a new economic growth point and industrial development model. It is also necessary to seize the opportunity that enterprises from all walks of life have realized the urgency and importance of digital transformation to guide the industry and encourage enterprises, thereby flexibly implementing digital transformation strategies according to their own needs.


### 3.6. Digital Social Governance

The digital social governance index value of Shaanxi Province in 2015 was set to 100, and the evaluation results of digital social governance from 2015 to 2021 are shown in Figure9.Figure 9
Digital society governance evaluation results.The development of digital social governance in Shaanxi Province was relatively slow, and the index value did not change significantly from 2015 to 2018. In 2019 and 2020, the index value increased slightly, and in 2021, digital social governance developed rapidly. The digital social governance index value in 2020 was 127.93, and the index value in 2021 greatly increased to 185.44. To further enhance the construction of digital social governance, it is necessary to speed up the construction of digital government and improve the new system of data integration and sharing to vigorously develop e-government and facilitate the development and reuse of data value-added, which improve e-government-related laws and regulations and overall coordination mechanisms and improve the public data open system, thus ensuring the healthy development of e-government.


### 3.7. Development Forecast of Digital Economy

The digital economy development plan was adjusted according to the evaluation results of the digital economy development, and the development status of the digital economy in Shaanxi Province was evaluated according to the adjusted data, comparing it with the data in 2021. The results are shown in Figure10.Figure 10
Digital economy development forecast.After the adjustment, the index values of digital infrastructure, digital resource elements, digital technology innovation, digital industry development, digital industry application, and digital social governance have all improved. The comprehensive evaluation of the digital economy development in Shaanxi Province before the adjustment was 72.38, and the comprehensive evaluation after the adjustment was 79.49. The digital economy development in Shaanxi Province increased by 9.82%.


## 3.1. Digital Infrastructure

The digital infrastructure index value of Shaanxi Province in 2015 is set to 100, and the evaluation results of digital infrastructure from 2015 to 2021 are shown in Figure4.Figure 4
Digital infrastructure evaluation results.The evaluation results of digital infrastructure in Shaanxi Province in 2016 and 2017 were relatively close to those in 2015, and the development of digital infrastructure in these two years has not changed much. Since 2018, Shaanxi Province has increased its investment in digital infrastructure, and the evaluation results of digital infrastructure have improved year by year. Especially in 2020 and 2021, the evaluation results of digital infrastructure have increased significantly. The evaluation result in 2019 was 117.83, which was raised to 132.58 in 2020 and 146.69 in 2021. It can be seen that the development of digital infrastructure in Shaanxi Province has made great progress in the past two years. In order to further improve the construction of digital infrastructure, it is necessary to integrate the network equipment of the four major operators, including Telecom, China Mobile, China Unicom, and Radio and Television, increasing investment in various fields of “new infrastructure” and accelerating the pace of construction of new infrastructure such as data centers and 5 G networks, to improve network equipment and implement speed-up and fee reduction.


## 3.2. Elements of Digital Resources

Setting the index value of digital resource elements in Shaanxi Province in 2015 to 100, the evaluation results of digital resource elements from 2015 to 2021 are shown in Figure5.Figure 5
Evaluation results of digital resource elements.The development of digital resource elements in Shaanxi Province was relatively slow in 2016 and 2017, but has been greatly improved in 2018. It increased from 108.69 in 2017 to 119.74 and then maintained a relatively fast growth rate year after year. In 2021, the evaluation result of digital resource elements was improved to 136.84. To further enhance the construction of digital resource elements, it is necessary to accelerate the construction of an intelligent society, with the interconnection of different operating systems as the main line, and to promote the co-construction, sharing of software and hardware facilities.


## 3.3. Digital Technology Innovation

The digital technology innovation index value of Shaanxi Province in 2015 was set to 100, and the evaluation results of digital technology innovation from 2015 to 2021 are shown in Figure6.Figure 6
Digital technology innovation evaluation results.The value of the digital technology innovation index in Shaanxi Province showed a gradually increasing trend, and its growth rate was the fastest in 2019 and 2020. The index value of digital technology innovation in 2019 was 116.69. The index value in 2020 was raised to 123.45, and it was 127.85 in 2021. To further enhance the construction of digital technology innovation, it is necessary to further increase the research and development investment intensity of digital economy enterprises and introduce digital economy professionals.


## 3.4. Development of Digital Industry

The digital industry development index value of Shaanxi Province in 2015 was set to 100, and the evaluation results of digital industry development from 2015 to 2021 are shown in Figure7.Figure 7
Digital industry development evaluation results.The development speed of the digital industry was relatively slow, showing a steady upward trend as a whole. In 2021, the development index value of the digital industry in Shaanxi Province was 157.36, and the development trend was good. To further enhance the construction of digital industry development, it is necessary to integrate good industries such as electronic information industry and software service industry to improve the effect of industrial integration. At the same time, relying on the existing industrial foundation and scientific and technological research and development advantages, the construction of digital industrial parks and bases needs to be continuously strengthened, and the radiation effect of the park should be enhanced.


## 3.5. Digital Industry Applications

The digital industry application index value of Shaanxi Province in 2015 was set to 100, and the evaluation results of digital industry application in 2015–2021 are shown in Figure8.Figure 8
Digital industry application evaluation results.In terms of digital industry applications, the development of digital industry applications was relatively slow from 2015 to 2018. It improved in 2019 and significantly improved in 2020 and 2021. The value of the digital industry application index in 2020 was 137.35, and the index value in 2021 was 174.93. To further enhance the construction of digital industry development, it is necessary to break industrial boundaries to promote industrial integration and industrial model innovation, forming a new economic growth point and industrial development model. It is also necessary to seize the opportunity that enterprises from all walks of life have realized the urgency and importance of digital transformation to guide the industry and encourage enterprises, thereby flexibly implementing digital transformation strategies according to their own needs.


## 3.6. Digital Social Governance

The digital social governance index value of Shaanxi Province in 2015 was set to 100, and the evaluation results of digital social governance from 2015 to 2021 are shown in Figure9.Figure 9
Digital society governance evaluation results.The development of digital social governance in Shaanxi Province was relatively slow, and the index value did not change significantly from 2015 to 2018. In 2019 and 2020, the index value increased slightly, and in 2021, digital social governance developed rapidly. The digital social governance index value in 2020 was 127.93, and the index value in 2021 greatly increased to 185.44. To further enhance the construction of digital social governance, it is necessary to speed up the construction of digital government and improve the new system of data integration and sharing to vigorously develop e-government and facilitate the development and reuse of data value-added, which improve e-government-related laws and regulations and overall coordination mechanisms and improve the public data open system, thus ensuring the healthy development of e-government.


## 3.7. Development Forecast of Digital Economy

The digital economy development plan was adjusted according to the evaluation results of the digital economy development, and the development status of the digital economy in Shaanxi Province was evaluated according to the adjusted data, comparing it with the data in 2021. The results are shown in Figure10.Figure 10
Digital economy development forecast.After the adjustment, the index values of digital infrastructure, digital resource elements, digital technology innovation, digital industry development, digital industry application, and digital social governance have all improved. The comprehensive evaluation of the digital economy development in Shaanxi Province before the adjustment was 72.38, and the comprehensive evaluation after the adjustment was 79.49. The digital economy development in Shaanxi Province increased by 9.82%.


## 4. Conclusions

This study used machine learning algorithms to study the evaluation of the development of the digital economy, so as to grasp the law of the development of the digital economy for Shaanxi Province and build a digital economy monitoring and evaluation system according to local conditions, thereby effectively monitoring and evaluating the development of the digital economy and providing reference. This study first described the dimensions of digital economy monitoring and evaluation, including six parts: digital infrastructure, data resource elements, digital technology innovation, digital industry development, digital industry application, and digital social governance. Then, the digital economy evaluation index was established, and then, the digital economy evaluation model was established. Based on the evaluation results of the digital economy, this study also puts forward suggestions on the digital economy planning of Shaanxi Province. According to the adjusted plan, the development of digital economy in Shaanxi Province was re-evaluated. The results showed that the evaluation model can grasp the development law of the digital economy and provide reference for the development of the digital economy.



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*Source: 6327347-2022-10-04.xml*