# Prediction and Industrial Structure Analysis of Local GDP Economy Based on Machine Learning

Authors: Zhiqiang Jiang
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

The process of regional economic growth is a long-term evolutionary law. During this long evolutionary process, some regions may continue to grow, while others may fall into decline. It takes a long time. For example, from the perspective of our country’s regional economic growth since the turn of the century, the east coast has been in a relatively developed state, while the economy of some western regions is relatively backward. Therefore, how to promote the long-term growth of developed regions and revitalize the troubled regional economy by studying the long-term growth mechanism of the regional economy is an important topic of regional economic research. In this context, we can draw the following conclusions. (1) The employment structure of major industries has been declining year by year since 2000, and this trend is relatively obvious and the decline is relatively large. Despite some changes in industrial growth, the overall trend is upward. The employment structure of the service industry has increased year by year, and its proportion in total employment usually exceeds that of major industries, and it is the industry with the largest number of employees. (2) The accuracy under the machine learning model is 79.46%, the reliability is 89.27%, and it is feasible; the accuracy under the data mining model is 68.45%, the reliability is 75.43%, and the feasibility is 86.18%; the accuracy rate under the traditional statistical model is 60.14%, the feasibility is 68.24%, and the reliability is 75.12%. GDP not only is the core indicator of national economic accounting but also can be used to measure the economic status and development level of a country or region. The impact of industrial structure on GDP is huge, and a suitable industrial structure can promote a healthier growth of GDP. In order to analyze the relationship between our country’s GDP and industrial structure, the quantitative analysis method of grey correlation analysis is used to study it, and then according to the calculation results, suggestions for adjusting and optimizing the industrial structure will be put forward to the relevant ministries.


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## Body

## 1. Introduction

In this survey, we review the work of machine learning on methods for dealing with datasets that contain large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features and the problem of selecting relevant examples. We describe the progress made on these topics in empirical and theoretical work in machine learning and propose a general framework for comparing different approaches. We conclude with some challenges for future work in this field [1]. Interpretation of brain imaging experiments requires analysis of complex multidimensional data. In recent years, the use of machine learning algorithms has become an increasingly popular diagnostic technique for training classifiers to extract fMRI data of stimuli, mental states, behaviors, and other variables of interest and to show that the data contain information about them. In reviewing this tutorial, we will understand some of the key options involved in using this technique and how to obtain statistically significant results, illustrating each point with a concrete example [2]. The repeatability and efficiency of the corner detector determines its likelihood of being useful in practical applications. Repeatability is important because the same scene viewed from different locations should yield features corresponding to the same real-world 3D location. Efficiency is important because it determines whether the detector combined with further processing can run at frame rate. The article describes three advances. First, we propose a new feature detection heuristic, and using machine learning, we derive a feature detector from it [3]. We review machine learning methods using positive definite kernels. These methods formulate learning and estimation problems in reproducing kernel Hilbert spaces (RKHSs) of functions defined over the data domain and are extended in terms of kernels. Working in the linear space of functions facilitates the construction and analysis of learning algorithms, while allowing for large classes of functions. The latter includes non-linear functions as well as functions defined on non-vector data. We cover a wide range of methods, from binary classifiers to complex structured data estimation methods [4]. Feature selection algorithms fall into two broad categories: wrappers that use self-learning algorithms to rank features by their usefulness, and filters that rank features based on heuristics that share features in the data. For applications with large databases, filters have proven to be more practical than wrappers because they are much faster. However, most existing filtering algorithms are only suitable for discrete classification problems. The article describes a fast, correlation-based filter algorithm that can be applied to both continuous and discrete problems [5]. This article discusses factor models that use multiple real-time monthly and quarterly time series to forecast GDP growth in the short term. Factors are estimated using the EM algorithm with basic component estimators, taking into account the different time periods of the data and missing observations at the end of the sample. We discuss some characteristics of real-time estimator samples and provide an alternative approach to forecasting quarterly GDP using monthly ratios [6]. The article compares the performance of a monthly follower model GDP forecast based on time series of the French economy. These models are based on static and dynamic foundations obtained by time and frequency level methods. We question whether it is more appropriate to use aggregated or disaggregated data to derive the factors used in the prediction equations. The accuracy of the forecast for different forecast horizons was evaluated taking into account sliding and recursive patterns [7]. To forecast several epochs into the future, the modeler is faced with a choice: iterative one-step ahead forecasting (IMS technique), or directly model the relationship between observations separated by a periodic interval and use it for forecast (DMS forecast). Structural fractures, unit root non-stationarity, and residual autocorrelation are known to improve DMS accuracy in finite samples, all of which occurred when modeling South African GDP for the period 1965 to 2000 [8]. The impact of monetary policy on the economy has a long and variable lag, so policymakers need reliable forecasts of economic activity. As a result, forecasts of real GDP growth have become increasingly necessary. A new modified ARIMA model is proposed and used to predict China’s GDP growth from 1978 to 2004 [9]. Aiming at the development of GDP in the process of urbanization in Zhoushan, establish a GDP measurement model in Zhoushan, open up the measurement relationship between the development of Zhoushan’s marine economy and the statistical law of employment growth, and establish a GDP measurement model in Zhoushan. The impact of GDP growth on employment puts forward the predictable stimulating results of Zhoushan’s marine economic development on employment, which provides theoretical and practical reference for promoting Zhoushan’s urbanization [10]. The growth of the total economic volume is based on the growth of various industrial sectors. The modern economic growth mode is essentially structure-oriented growth, that is, the core of economic growth is the change of industrial structure. The effectiveness of these factors directly affects economic growth. Therefore, it is very important to examine the impact of industrial scale and efficiency factors on economic growth from the perspective of industrial structure. Scholars at home and abroad have done a lot of research on this topic, using a lot of research methods [11]. Different industries and lifestyles use water in different ways, and different economic development models dramatically change water demand. Therefore, simultaneous (dynamic) analysis of macroeconomic and water resource systems can reveal their intrinsic links. Taking Beijing as an example, this paper analyzes the relationship between industrial structure adjustment and water consumption growth by using relevant theories and analytical methods. Therefore, it is very important to develop the integration of the tertiary industry and the secondary industry to solve the water shortage problem in Beijing [12]. The data from 1978 to 2004 are used as a sample to conduct an empirical analysis on the relationship between urbanization and industrialization. The results show that there is a long-term equilibrium relationship between China’s urbanization and industrial structure. Different from their short-term relationship, the one-way Granger causality and their respective variances have inertia [13]. With the help of marine statistical data, the article examines the current situation and development trend of our country’s marine industry structure. With the help of econometric methods such as correlation analysis and grey phenomenon theory, the essential factors affecting the growth of the shipping industry are clarified. According to the “Outline of National Marine Economic Development Plan,” analyze industrial economy, industrial phenomenon theory, regional economy, and integration of shipping and agriculture, combined with structural problems and measures of our country’s marine industry, to provide good technology for the development of marine industry in China [14]. The price structure of forest production in China from 1996 to 2009 was examined using grey relational analysis. Based on the GM(1.1) model, a data model for predicting China’s forestry structure in the next 10 years was established. The results show that the grey correlation coefficients between the three forestry industries and forestry output value are 0.8491, 0.7311, and 0.8213, respectively, in the order of secondary industry < tertiary industry < large industry. The prediction results show that our country’s forestry is in the middle stage of industrialization, and the secondary and tertiary industries have developed rapidly and become the main industries [15].


## 2. Analysis of Local Economic and Industrial Structure under Machine Learning

### 2.1. Problems Existing in the Economic and Industrial Structure

(1)
Convergence of regional industrial structure: that is, the similarity rate of industrial structure in the eastern, central, and western regions is relatively high. To adjust our country’s current industrial structure, we should follow the basic principles of combining market regulation and government guidance, improving the level of industrial technology through independent innovation, adhering to a new path of industrialization, and promoting the coordinated and healthy development of industries. Continue to promote the construction of energy, transportation, and communication infrastructure, accelerate the adjustment and optimization of industrial product structure, vigorously expand the tertiary industry to absorb labor employment as the main goal, and take practical and feasible adjustment measures.(2)
According to the actual situation of each region, actively learn from advanced experience, rely on high-quality industries, optimize and adjust the industrial structure, and greatly improve the economic level of the whole region.With the continuous growth of the economy, capital and labor are transferred to the secondary or tertiary industries, which leads to changes in the industrial structure of the regional economy. The three industries are not coordinated. Development is modernization and industrial transformation. Years of practical experience in adjusting the regional economic and business structure have shown that in the face of these two problems, we must take a holistic approach. For a long time, the primary and secondary industries have been the main body of the market. In order to change the backward situation of our country’s “manufacturing industry,” actively promote industrial innovation and increase the market share of the tertiary industry. In the process of development and reform, we adhere to the principle of “less but fine” and pay attention to the actual level and standard of the business as well as the structure of the industry. According to the actual situation of each region, actively learn from advanced experience, rely on high-quality industries, optimize and adjust the industrial structure, and greatly improve the economic level of the whole region. It is shown in Figure1.Figure 1
Problems existing in the economic and industrial structure.


### 2.2. Adjustment Direction of Regional Economic and Industrial Structure

Industry is the most important sector supporting the national economy. As the main body of the national economy, the county economy should pay more attention to the development of industrialization. It is also the main direction of today’s industrial transformation and meets the needs of our country’s economic development. Due to the different economic conditions and industrial development levels in different countries, the actual adaptation process must be realistic and not be successful too quickly but must be gradually improved in industrialized industries. Considering that the current industry is developing in the direction of adapting to the county economy and business structure, we think there are mainly the following reasons. (1) The development of the service industry and service industry is inseparable from industrial support. It is necessary to use the service industry to drive social concerns and promote economic development and structural adjustment. (2) The demand for service industry in small areas is lower than that in cities. The development of service industry needs to effectively manage the type and scale of service industry in combination with regional characteristics. The over-allocation and waste of resources in the province have created a harsh environment for market competition. (3) According to relevant data, the economic benefits of the manufacturing industry are among the best in many industries, and it has obvious development advantages in terms of national taxation and employment, providing a circular economy for the sustainable development of the industry. Regional industries realize high stability of circular economy. When the state manages the economic restructuring of the whole province, industry not only has an important impact on economic development but also has a good leading role in other industries. The development of high-quality industries can guide many industries, such as the service industry, and is the primary choice for industrial restructuring and development.


### 2.3. Breakthroughs in Industrial Structure Adjustment from Key Positions

Under the new situation, the phenomenon of overcapacity is mainly manifested in the process of regional adaptation of the economic and commercial structure, which exists objectively and is not a random phenomenon. Recovery will come at a huge price. In the process of economic manufacturing, some regional industries do not do temporary work well without new production technology and unique profile products, which brings great challenges to the industry. The following points are investigated and evaluated. (1) From the beginning, comprehensively plan the existing business in the region and implement the upgrading and renewal of the industry based on this. Actively respond to the government’s call and adhere to the development policy of “focusing on development, common improvement, and comprehensive coordination.” In the early stage before the actual operation, it is necessary to make preparations on the basis of the existing business structure, put forward the basic development policy, and select industries and enterprises for modernization and innovation. Initially, it focused on technological innovation, equipment upgrades, and product improvements to simplify the process, improve economics, and realize it. Find and deliver the most accurate and personalized guidance to help you succeed. (2) Focus on promoting and developing the industry. Strengthen the improvement of basic equipment and core technologies of relevant enterprises and focus on scientific and technological research and product innovation research. At present, the regionalized production of a large number of imported high-tech equipment and materials in our country not only increases the production cost but also wastes a lot of financial resources. Therefore, it is necessary to actively support the development of science and technology, organize high-tech talents to participate in scientific and technological innovation, participate in major achievements and technologies, and reduce the impact of monopoly technology on enterprise development. Of course, development is inseparable from cooperation and active technological integration. We must focus on promoting the transformation of primary and secondary industries to high-level industries and promote economic and industrial restructuring.


### 2.4. Economic and Industrial Forecasting Steps

Economic forecasts are speculations and estimates of future scenarios for economic phenomena. According to the history and status quo of the economic development process, the scientific forecasting method shows the regularity of the development of economic phenomena and the connection between various economic phenomena and predicts the future development trend and the possible level of the economic phenomenon. The content of economic forecast is very broad: the first is the forecast of domestic economic situation, such as production development, growth rate, economic structure, price development, population employment, tax revenue and expenditure development, supply, production, and sales. At the same time, the international economic situation should also be taken into account, such as the prediction of international economic fluctuations and changes in the international market in region B. Your forecast usually consists of four steps: collecting and analyzing the various data required for the forecast, performing various forecast calculations to outline the preliminary forecast, completing and revising the forecast, and preparing a formal forecast report. The above process is a recurring prognostic process. This cycle typically occurs twice a year, and a forecast report is generated approximately every six months. It is shown in Figure2.Figure 2
Economic and industrial forecasting steps.


### 2.5. Keep Improving and Become Bigger and Stronger

As part of the structural adjustment, special jobs should be reserved for regional industries. The Swiss watch industry, for example, is small but has unique high-end brands that have huge growth advantages. We must actively learn from and learn from, focus on developing high-quality industries, make them bigger and stronger, promote industrial transformation, and promote the integration of regional economy and industrial structure. Our country believes that it is very important to adjust the economic and industrial structure of the region, overcome technical difficulties, and vigorously carry out scientific research. At the same time, it is necessary to fill the gaps in core industries, closely follow the characteristics of industries, make them bigger and stronger, and actively promote the process of regional economic and trade structure integration.


## 2.1. Problems Existing in the Economic and Industrial Structure

(1)
Convergence of regional industrial structure: that is, the similarity rate of industrial structure in the eastern, central, and western regions is relatively high. To adjust our country’s current industrial structure, we should follow the basic principles of combining market regulation and government guidance, improving the level of industrial technology through independent innovation, adhering to a new path of industrialization, and promoting the coordinated and healthy development of industries. Continue to promote the construction of energy, transportation, and communication infrastructure, accelerate the adjustment and optimization of industrial product structure, vigorously expand the tertiary industry to absorb labor employment as the main goal, and take practical and feasible adjustment measures.(2)
According to the actual situation of each region, actively learn from advanced experience, rely on high-quality industries, optimize and adjust the industrial structure, and greatly improve the economic level of the whole region.With the continuous growth of the economy, capital and labor are transferred to the secondary or tertiary industries, which leads to changes in the industrial structure of the regional economy. The three industries are not coordinated. Development is modernization and industrial transformation. Years of practical experience in adjusting the regional economic and business structure have shown that in the face of these two problems, we must take a holistic approach. For a long time, the primary and secondary industries have been the main body of the market. In order to change the backward situation of our country’s “manufacturing industry,” actively promote industrial innovation and increase the market share of the tertiary industry. In the process of development and reform, we adhere to the principle of “less but fine” and pay attention to the actual level and standard of the business as well as the structure of the industry. According to the actual situation of each region, actively learn from advanced experience, rely on high-quality industries, optimize and adjust the industrial structure, and greatly improve the economic level of the whole region. It is shown in Figure1.Figure 1
Problems existing in the economic and industrial structure.


## 2.2. Adjustment Direction of Regional Economic and Industrial Structure

Industry is the most important sector supporting the national economy. As the main body of the national economy, the county economy should pay more attention to the development of industrialization. It is also the main direction of today’s industrial transformation and meets the needs of our country’s economic development. Due to the different economic conditions and industrial development levels in different countries, the actual adaptation process must be realistic and not be successful too quickly but must be gradually improved in industrialized industries. Considering that the current industry is developing in the direction of adapting to the county economy and business structure, we think there are mainly the following reasons. (1) The development of the service industry and service industry is inseparable from industrial support. It is necessary to use the service industry to drive social concerns and promote economic development and structural adjustment. (2) The demand for service industry in small areas is lower than that in cities. The development of service industry needs to effectively manage the type and scale of service industry in combination with regional characteristics. The over-allocation and waste of resources in the province have created a harsh environment for market competition. (3) According to relevant data, the economic benefits of the manufacturing industry are among the best in many industries, and it has obvious development advantages in terms of national taxation and employment, providing a circular economy for the sustainable development of the industry. Regional industries realize high stability of circular economy. When the state manages the economic restructuring of the whole province, industry not only has an important impact on economic development but also has a good leading role in other industries. The development of high-quality industries can guide many industries, such as the service industry, and is the primary choice for industrial restructuring and development.


## 2.3. Breakthroughs in Industrial Structure Adjustment from Key Positions

Under the new situation, the phenomenon of overcapacity is mainly manifested in the process of regional adaptation of the economic and commercial structure, which exists objectively and is not a random phenomenon. Recovery will come at a huge price. In the process of economic manufacturing, some regional industries do not do temporary work well without new production technology and unique profile products, which brings great challenges to the industry. The following points are investigated and evaluated. (1) From the beginning, comprehensively plan the existing business in the region and implement the upgrading and renewal of the industry based on this. Actively respond to the government’s call and adhere to the development policy of “focusing on development, common improvement, and comprehensive coordination.” In the early stage before the actual operation, it is necessary to make preparations on the basis of the existing business structure, put forward the basic development policy, and select industries and enterprises for modernization and innovation. Initially, it focused on technological innovation, equipment upgrades, and product improvements to simplify the process, improve economics, and realize it. Find and deliver the most accurate and personalized guidance to help you succeed. (2) Focus on promoting and developing the industry. Strengthen the improvement of basic equipment and core technologies of relevant enterprises and focus on scientific and technological research and product innovation research. At present, the regionalized production of a large number of imported high-tech equipment and materials in our country not only increases the production cost but also wastes a lot of financial resources. Therefore, it is necessary to actively support the development of science and technology, organize high-tech talents to participate in scientific and technological innovation, participate in major achievements and technologies, and reduce the impact of monopoly technology on enterprise development. Of course, development is inseparable from cooperation and active technological integration. We must focus on promoting the transformation of primary and secondary industries to high-level industries and promote economic and industrial restructuring.


## 2.4. Economic and Industrial Forecasting Steps

Economic forecasts are speculations and estimates of future scenarios for economic phenomena. According to the history and status quo of the economic development process, the scientific forecasting method shows the regularity of the development of economic phenomena and the connection between various economic phenomena and predicts the future development trend and the possible level of the economic phenomenon. The content of economic forecast is very broad: the first is the forecast of domestic economic situation, such as production development, growth rate, economic structure, price development, population employment, tax revenue and expenditure development, supply, production, and sales. At the same time, the international economic situation should also be taken into account, such as the prediction of international economic fluctuations and changes in the international market in region B. Your forecast usually consists of four steps: collecting and analyzing the various data required for the forecast, performing various forecast calculations to outline the preliminary forecast, completing and revising the forecast, and preparing a formal forecast report. The above process is a recurring prognostic process. This cycle typically occurs twice a year, and a forecast report is generated approximately every six months. It is shown in Figure2.Figure 2
Economic and industrial forecasting steps.


## 2.5. Keep Improving and Become Bigger and Stronger

As part of the structural adjustment, special jobs should be reserved for regional industries. The Swiss watch industry, for example, is small but has unique high-end brands that have huge growth advantages. We must actively learn from and learn from, focus on developing high-quality industries, make them bigger and stronger, promote industrial transformation, and promote the integration of regional economy and industrial structure. Our country believes that it is very important to adjust the economic and industrial structure of the region, overcome technical difficulties, and vigorously carry out scientific research. At the same time, it is necessary to fill the gaps in core industries, closely follow the characteristics of industries, make them bigger and stronger, and actively promote the process of regional economic and trade structure integration.


## 3. Machine Learning Algorithms

### 3.1. Classical Machine Learning Algorithms

KNN algorithm, random forest algorithm, and SVM algorithm are three classic machine learning algorithms in supervised learning. They are commonly used in various intrusion detection scenarios. This section mainly introduces their respective classification ideas. If most of K’s nearest neighbors belong to a particular class, then the sample also belongs to that class, and that class contains the properties of that sample. The distance formula to calculate the distance between two samples is(1)Lpxi,xj=∑l=1nxi−xjp1/p,(2)Fx=−1,gx,+1,otherwise.In this attack scenario, the attacker aims to collect proxy records containing malicious samples and normal samples and find out the feature vector space of the target model and the corresponding information parameter vector. If most of K’s nearest neighbors belong to a particular class, then the sample also belongs to that class, and that class contains the properties of that sample. After the calculation of the result formula (2), the accuracy of the classical machine learning algorithm will be improved even more.In this case, attackers typically manipulate malicious samples according to conditional probability distributions or feature space distances to get as close as possible to the protected data.(3)z∗=argmingx=argminωTxj.Accuracy is the percentage of correctly predicted samples to the total number of samples; response rate is the ratio of the number of positive samples found to the total number of positive samples, reflecting insufficient sample coverage; percentage accuracy means that all predicted positive samples are true. The proportion of positive samples may reflect the problem of false positives. TheF1 score is the harmonic mean of recall and precision and is the combined score of recall and precision. The calculation formulas of the four indicators of accuracy, accuracy, recall, and F1 score are as follows:(4)accuracy=TP+TNTP+FN+FP+TN,precisiom=TPTP+FP,recall=TPTP+FN,F1score=2∗precision∗recallprecision+recall.In order to improve the classification accuracy, the corresponding loss function is often developed, and the optimal weight value and paranoia value of the function are obtained by finding the maximum value of the function:(5)minL=minϖ22+c∑i=1nmax0,1−yigxi,minL=minϖ22+c∑i=1nmax0,1−yiwTxi+b,s.t.∑i=1dwi−w2k≤wt.


### 3.2. KNN Algorithm Model

The KNN algorithm continuously finds the best values ofw and b during the analysis process, so that the loss of the function reaches the lowest value. The training process randomly assigns final weights. The training process randomly assigns final weights. This training method has no problem in the natural environment, but in a conflict situation, you will be attacked. Attackers can take advantage of this and create malicious samples appropriately to misclassify them. The output can be a combination of several binary decisions, such as whether to evaluate a pixel as the foreground and background of the image, or a more structured output such as a syntax tree or containing box. Multiple binary outputs can simply be represented by multiple independent SVMs.(6)∇ωL≅ω+c∑ies∇lixi,∇ωL≅c∑ies∇li.To visualize the security of classification algorithms, a new concept is introduced here: classifier sensitivity. The higher the sensitivity of the classification, the less secure the algorithm, and vice versa. The security of this algorithm is described as follows:(7)ΔFx,x′=Fx−Fx′|x−x′|≤1k∑K=1Kωk,dydt=kyy−yi,Cj=∑i∈cjti∗i∑i∈cjti.When a user selects a service that is interested in interaction, the relevant information of the service can be retrieved, and changes in the community to which the service belongs are highlighted in the adjacent spatiotemporal dimensions.


### 3.3. K-Means Machine Learning Model

The K-means algorithm is an unsupervised learning algorithm that can be applied to unlabeled data. The goal of the algorithm is to find datasets whose number is determined by K. The algorithm iteratively assigns data to one of K groups based on the provided features. Group data based on data similarity. In the network model of the query system, the prescribed fusion rules are used to integrate the interactive big data of the educational administration system, so as to realize the interactive query of the big data.The convergence control function of the interactive big data query in the educational administration system is(8)Ft=∑j=1zjT+h−f2−sin2πt,where h represents the characteristic sampling frequency of the educational administration system and f is the information transmission rate of the educational administration system data query. The output can be a combination of several binary decisions, such as whether to evaluate a pixel as the foreground and background of the image, or a more structured output such as a syntax tree or containing box. Multiple binary outputs can simply be represented by multiple independent SVMs.Based on this, the window width of the interactive big data transmission of the educational administration system is calculated. The calculation formula is(9)um=∑j=1zjTy+q−Ft,where y is the time delay of data transmission and q is the hidden node of data transmission. Using the adaptive deep learning method, the transmission frequency of interactive big data query data in the educational administration system is obtained as follows:(10)ϕ=−πFt2y+q+log3η+S2c+q.In the formula, ϕ is the gradient error of data transmission;r is the iterative step size of deep learning. Through the method of reconstructing the similarity feature of the target sample set, the deep learning of the interactive big data model of the educational administration system is realized, and the global optimal control function of the model is obtained as follows:(11)Y=∑n=1,t=1μnr+o−pt+py2tπ2,where o is the step size of adaptive learning. Finally, the optimal model of interactive big data in the educational administration system is obtained as follows:(12)Fk=Ft+∑n=1,t=1μnr−pt.


## 3.1. Classical Machine Learning Algorithms

KNN algorithm, random forest algorithm, and SVM algorithm are three classic machine learning algorithms in supervised learning. They are commonly used in various intrusion detection scenarios. This section mainly introduces their respective classification ideas. If most of K’s nearest neighbors belong to a particular class, then the sample also belongs to that class, and that class contains the properties of that sample. The distance formula to calculate the distance between two samples is(1)Lpxi,xj=∑l=1nxi−xjp1/p,(2)Fx=−1,gx,+1,otherwise.In this attack scenario, the attacker aims to collect proxy records containing malicious samples and normal samples and find out the feature vector space of the target model and the corresponding information parameter vector. If most of K’s nearest neighbors belong to a particular class, then the sample also belongs to that class, and that class contains the properties of that sample. After the calculation of the result formula (2), the accuracy of the classical machine learning algorithm will be improved even more.In this case, attackers typically manipulate malicious samples according to conditional probability distributions or feature space distances to get as close as possible to the protected data.(3)z∗=argmingx=argminωTxj.Accuracy is the percentage of correctly predicted samples to the total number of samples; response rate is the ratio of the number of positive samples found to the total number of positive samples, reflecting insufficient sample coverage; percentage accuracy means that all predicted positive samples are true. The proportion of positive samples may reflect the problem of false positives. TheF1 score is the harmonic mean of recall and precision and is the combined score of recall and precision. The calculation formulas of the four indicators of accuracy, accuracy, recall, and F1 score are as follows:(4)accuracy=TP+TNTP+FN+FP+TN,precisiom=TPTP+FP,recall=TPTP+FN,F1score=2∗precision∗recallprecision+recall.In order to improve the classification accuracy, the corresponding loss function is often developed, and the optimal weight value and paranoia value of the function are obtained by finding the maximum value of the function:(5)minL=minϖ22+c∑i=1nmax0,1−yigxi,minL=minϖ22+c∑i=1nmax0,1−yiwTxi+b,s.t.∑i=1dwi−w2k≤wt.


## 3.2. KNN Algorithm Model

The KNN algorithm continuously finds the best values ofw and b during the analysis process, so that the loss of the function reaches the lowest value. The training process randomly assigns final weights. The training process randomly assigns final weights. This training method has no problem in the natural environment, but in a conflict situation, you will be attacked. Attackers can take advantage of this and create malicious samples appropriately to misclassify them. The output can be a combination of several binary decisions, such as whether to evaluate a pixel as the foreground and background of the image, or a more structured output such as a syntax tree or containing box. Multiple binary outputs can simply be represented by multiple independent SVMs.(6)∇ωL≅ω+c∑ies∇lixi,∇ωL≅c∑ies∇li.To visualize the security of classification algorithms, a new concept is introduced here: classifier sensitivity. The higher the sensitivity of the classification, the less secure the algorithm, and vice versa. The security of this algorithm is described as follows:(7)ΔFx,x′=Fx−Fx′|x−x′|≤1k∑K=1Kωk,dydt=kyy−yi,Cj=∑i∈cjti∗i∑i∈cjti.When a user selects a service that is interested in interaction, the relevant information of the service can be retrieved, and changes in the community to which the service belongs are highlighted in the adjacent spatiotemporal dimensions.


## 3.3. K-Means Machine Learning Model

The K-means algorithm is an unsupervised learning algorithm that can be applied to unlabeled data. The goal of the algorithm is to find datasets whose number is determined by K. The algorithm iteratively assigns data to one of K groups based on the provided features. Group data based on data similarity. In the network model of the query system, the prescribed fusion rules are used to integrate the interactive big data of the educational administration system, so as to realize the interactive query of the big data.The convergence control function of the interactive big data query in the educational administration system is(8)Ft=∑j=1zjT+h−f2−sin2πt,where h represents the characteristic sampling frequency of the educational administration system and f is the information transmission rate of the educational administration system data query. The output can be a combination of several binary decisions, such as whether to evaluate a pixel as the foreground and background of the image, or a more structured output such as a syntax tree or containing box. Multiple binary outputs can simply be represented by multiple independent SVMs.Based on this, the window width of the interactive big data transmission of the educational administration system is calculated. The calculation formula is(9)um=∑j=1zjTy+q−Ft,where y is the time delay of data transmission and q is the hidden node of data transmission. Using the adaptive deep learning method, the transmission frequency of interactive big data query data in the educational administration system is obtained as follows:(10)ϕ=−πFt2y+q+log3η+S2c+q.In the formula, ϕ is the gradient error of data transmission;r is the iterative step size of deep learning. Through the method of reconstructing the similarity feature of the target sample set, the deep learning of the interactive big data model of the educational administration system is realized, and the global optimal control function of the model is obtained as follows:(11)Y=∑n=1,t=1μnr+o−pt+py2tπ2,where o is the step size of adaptive learning. Finally, the optimal model of interactive big data in the educational administration system is obtained as follows:(12)Fk=Ft+∑n=1,t=1μnr−pt.


## 4. Prediction and Industrial Structure Analysis of Machine Learning in Local GDP Economy

### 4.1. Local GDP Economic Forecast under Machine Learning

The process of regional economic growth is a long-term evolutionary law. During this long evolutionary process, some regions may continue to grow, while others may fall into decline. It takes a long time. For example, from the perspective of our country’s regional economic growth since the turn of the century, the east coast has been in a relatively developed state, while the economy of some western regions is relatively backward. Therefore, how to promote the long-term growth of developed regions and revitalize the troubled regional economy by studying the long-term growth mechanism of the regional economy is an important topic of regional economic research. Only population, wages, prices, resources, technology, and income distribution affect the long-term growth of regional economies. The flow of these factors, especially the flow of labor and capital, has a greater impact on regional economic growth. The determinants of regional growth can be divided into endogenous and exogenous factors. The main endogenous factors are the distribution and supply of production factors, such as the distribution and supply of land, labor, and capital. The main external factors are related to the evolution of commodity demand within the region, national investment, and national economy. According to the endogenous and exogenous elements of regional growth and their combinations, from the perspective of integrity and decentralization, distinguish complex models of regional growth, decentralization systems, regional convergence or differentiation growth issues, and social and political factors.From the data in Figure3, we can see that from 2017 to 2021, Guangdong Province and Tianjin City have a relatively large number of marine economic structures, indicating that their marine economic structures are more diversified, their marine economic development is relatively stable, and their economic structure is relatively stable. It has not changed much. The marine economy and industrial structure of Liaoning and Shandong have relatively few direct descendants, indicating that the value of marine industry products is relatively concentrated in one or more industries. The industry and business structure dominated by sea fishing will be simplified. The structure is strongly influenced by changes in several major industries, resulting in large fluctuations in overall development and relatively unstable economic development. The number of direct descendants of the industrial structure changed greatly. The impact of Liaoning and Shandong’s marine economy and industrial structure is relatively small compared with the previous few years, indicating that the marine economic structure was relatively simple at this time. The industrial structure of different provinces is quite different, which leads to the great fluctuation of the entropy of economic structure in different years. In 2021, the entropy of economic structure in each province is the highest, which indicates that the industrial structure is multidimensional. The large fluctuation in different years indicates that the industrial structure fluctuates greatly in different years, and the structure develops in multiple dimensions. When the change of the entropy of economic structure is small, it shows that the industrial structure depends on less industries.Figure 3
Calculation table of the number of direct descendants of the economic and industrial structure in different regions.From the data in Figure4, we can see that the accuracy under the KNN machine learning model is 79.46%, the feasibility is 89.27%, and the model score is 93.45%; the accuracy under the K-means machine learning model is 68.45%, the feasibility is 75.43%, and the model score is 86.18%; the accuracy under the FM machine learning model is 60.14%, the feasibility is 68.24%, and the model score is 75.12%. The KNN machine learning model has the highest accuracy and feasibility among the three models and is the best performing model among the three models.Figure 4
Performance tests under different machine learning models.From the data in Figure5 and Table 1, we can see that the actual value of GDP from 2016 to 2021 shows a steady upward trend, and the predicted values under different machine learning models are different. The predicted value of the s-svm model in 2016 was 48188, the predicted value of the K-means model was 47523, and the predicted value of the FM model was 47131; the predicted value of the s-svm model in 2017 was 51469, and the prediction number of the K-means model is 50279, and that of the FM model is 49946; the prediction number of the s-svm model in 2018 is 56751, that of the K-means model is 55361, and that of the FM model is 54988. In 2019, the number of predictions of the s-svm model is 61516, the number of predictions of the K-means model is 61109, and the number of forecasts of the FM model is 60723. It can be seen that the s-svm model and the predicted values are the closest to the actual values, and the error rate is the smallest The prediction value of the s-svm model is the closest to the real value in the prediction value of GDP by different algorithms. The prediction accuracy rate can reach more than 96%, and the highest is 99%, which has a high prediction effect. The prediction effect of FM model is poor, and the average prediction accuracy is about 95%. Different models have obvious differences and advantages in predicting GDP growth. Therefore, the S-SVM model has a good application effect in the prediction of GDP.Figure 5
GDP forecast and actual GDP under machine learning.Table 1
2016–2021 GDP forecast and actual GDP under machine learning.
Real GDPs-svm model predicted valueK-means model predictionsFM model predictionsAverage error rate (%)2016489464818847523471319.562017517515146950279499468.742018561975675155361549888.022019629666151661109607237.372020663096608265912651966.652021734387300872834717935.03From the data in Figure6, we can see that in 2011, the GDP of expenditure was 188.61, consumption expenditure was 136.56, household consumption was 114.13, and government consumption was 22.43; expenditure GDP was 229.31, consumption expenditure was 164.26, household consumption was 137.32, and government consumption was 26.94. In 2014, expenditure GDP was 242.32, consumption expenditure was 185.47, household consumption was 157.70, and government consumption was 27.77.Figure 6
2011–2021 GDP of consumer expenditure in different regions.


### 4.2. Forecast and Industrial Structure Analysis in Local GDP Economy

According to the input-output theory and related mathematical models, most countries in the world began to make input-output tables, and countries began to flexibly use the output model to analyze many important issues of national development. Our country is no exception. The input-output table is used to analyze the relationship between different sectors and end-user sectors in our country to understand the state of economic development. Therefore, many professors and researchers have devoted their efforts to describe the application and analysis of the EC + IO model in our country’s economic development. However, since our country does not compile the input-output table every year, we can make statistical improvements to the input-output table for the years that have not been compiled on the original basis. The yearbook data then calculate the overall performance of each department for each year. Compared with previous test results, our test will be more comprehensive, not bound by personal thoughts, and presented to the public more objectively and fairly. From the perspective of the country, it can be viewed more modestly and cautiously, and from the perspective of the troops, it can also be a more objective and fair analysis, which helps to better understand the development of our country. Therefore, the analysis of our country’s input-output table is very important for the country and the masses.From the data in Figure7 and Table 2, we can see that the employment structure of major industries has been declining year by year since 2000, and this trend is relatively obvious and the decline is relatively large. Despite some changes in industrial growth, the overall trend is upward. The employment structure of the service industry has increased year by year, and its proportion in total employment usually exceeds that of major industries, and it is the industry with the largest number of employees. Judging from the development situation at home and abroad, the employment structure of our country’s primary industry is still relatively high, the level of agricultural mechanization is insufficient, the employment of the processing industry is obviously sufficient and relatively insufficient, the industrialization still needs to be improved, and the service scope bears the heavy burden of work and needs to be vigorously developed. It needs to continue to grow, and the entire industry structure also needs to be optimized and improved. From 2016 to 2021, we can see that the GDP growth rate of the region is still relatively high, basically higher than the national growth level. Especially in the past two years, the national growth rate is relatively low, and the GDP growth rate of the region maintains a certain advantage. In the three industrial structures, the proportion of secondary industry is relatively large, and the contribution of growth rate and GDP growth rate is relatively large. In the whole economic structure, different industries have maintained positive growth, so it is necessary to adjust the industrial development strategy for the primary industry and then provide intelligent upgrading services for the primary industry.Figure 7
Three changes in industrial structure.Table 2
Three changes in industrial structure.
Primary industry (%)Secondary industry (%)Tertiary industry (%)GDP growth rate (%)201658.5012.508.4020177.808.2010.50920187.508.209.508.8020196.207.608.507.9020205.106.207.605.6020218.509.6010.209.50From the data in Figure8, we can see that the actual proportion of the first industry is 23%, the proportion of the second industry is 33%, and the proportion of the third industry is 17%. The proportion of the primary industry predicted by the s-svm model is 26.53%, the proportion of the primary industry predicted by the K-means model is 27.38%, and the proportion of the primary industry predicted by the FM model is 28.91%. It can be seen that the prediction value under the s-svm model is the most accurate among the three models.Figure 8
The proportion of industrial structure and its predicted value under the machine learning model.


## 4.1. Local GDP Economic Forecast under Machine Learning

The process of regional economic growth is a long-term evolutionary law. During this long evolutionary process, some regions may continue to grow, while others may fall into decline. It takes a long time. For example, from the perspective of our country’s regional economic growth since the turn of the century, the east coast has been in a relatively developed state, while the economy of some western regions is relatively backward. Therefore, how to promote the long-term growth of developed regions and revitalize the troubled regional economy by studying the long-term growth mechanism of the regional economy is an important topic of regional economic research. Only population, wages, prices, resources, technology, and income distribution affect the long-term growth of regional economies. The flow of these factors, especially the flow of labor and capital, has a greater impact on regional economic growth. The determinants of regional growth can be divided into endogenous and exogenous factors. The main endogenous factors are the distribution and supply of production factors, such as the distribution and supply of land, labor, and capital. The main external factors are related to the evolution of commodity demand within the region, national investment, and national economy. According to the endogenous and exogenous elements of regional growth and their combinations, from the perspective of integrity and decentralization, distinguish complex models of regional growth, decentralization systems, regional convergence or differentiation growth issues, and social and political factors.From the data in Figure3, we can see that from 2017 to 2021, Guangdong Province and Tianjin City have a relatively large number of marine economic structures, indicating that their marine economic structures are more diversified, their marine economic development is relatively stable, and their economic structure is relatively stable. It has not changed much. The marine economy and industrial structure of Liaoning and Shandong have relatively few direct descendants, indicating that the value of marine industry products is relatively concentrated in one or more industries. The industry and business structure dominated by sea fishing will be simplified. The structure is strongly influenced by changes in several major industries, resulting in large fluctuations in overall development and relatively unstable economic development. The number of direct descendants of the industrial structure changed greatly. The impact of Liaoning and Shandong’s marine economy and industrial structure is relatively small compared with the previous few years, indicating that the marine economic structure was relatively simple at this time. The industrial structure of different provinces is quite different, which leads to the great fluctuation of the entropy of economic structure in different years. In 2021, the entropy of economic structure in each province is the highest, which indicates that the industrial structure is multidimensional. The large fluctuation in different years indicates that the industrial structure fluctuates greatly in different years, and the structure develops in multiple dimensions. When the change of the entropy of economic structure is small, it shows that the industrial structure depends on less industries.Figure 3
Calculation table of the number of direct descendants of the economic and industrial structure in different regions.From the data in Figure4, we can see that the accuracy under the KNN machine learning model is 79.46%, the feasibility is 89.27%, and the model score is 93.45%; the accuracy under the K-means machine learning model is 68.45%, the feasibility is 75.43%, and the model score is 86.18%; the accuracy under the FM machine learning model is 60.14%, the feasibility is 68.24%, and the model score is 75.12%. The KNN machine learning model has the highest accuracy and feasibility among the three models and is the best performing model among the three models.Figure 4
Performance tests under different machine learning models.From the data in Figure5 and Table 1, we can see that the actual value of GDP from 2016 to 2021 shows a steady upward trend, and the predicted values under different machine learning models are different. The predicted value of the s-svm model in 2016 was 48188, the predicted value of the K-means model was 47523, and the predicted value of the FM model was 47131; the predicted value of the s-svm model in 2017 was 51469, and the prediction number of the K-means model is 50279, and that of the FM model is 49946; the prediction number of the s-svm model in 2018 is 56751, that of the K-means model is 55361, and that of the FM model is 54988. In 2019, the number of predictions of the s-svm model is 61516, the number of predictions of the K-means model is 61109, and the number of forecasts of the FM model is 60723. It can be seen that the s-svm model and the predicted values are the closest to the actual values, and the error rate is the smallest The prediction value of the s-svm model is the closest to the real value in the prediction value of GDP by different algorithms. The prediction accuracy rate can reach more than 96%, and the highest is 99%, which has a high prediction effect. The prediction effect of FM model is poor, and the average prediction accuracy is about 95%. Different models have obvious differences and advantages in predicting GDP growth. Therefore, the S-SVM model has a good application effect in the prediction of GDP.Figure 5
GDP forecast and actual GDP under machine learning.Table 1
2016–2021 GDP forecast and actual GDP under machine learning.
Real GDPs-svm model predicted valueK-means model predictionsFM model predictionsAverage error rate (%)2016489464818847523471319.562017517515146950279499468.742018561975675155361549888.022019629666151661109607237.372020663096608265912651966.652021734387300872834717935.03From the data in Figure6, we can see that in 2011, the GDP of expenditure was 188.61, consumption expenditure was 136.56, household consumption was 114.13, and government consumption was 22.43; expenditure GDP was 229.31, consumption expenditure was 164.26, household consumption was 137.32, and government consumption was 26.94. In 2014, expenditure GDP was 242.32, consumption expenditure was 185.47, household consumption was 157.70, and government consumption was 27.77.Figure 6
2011–2021 GDP of consumer expenditure in different regions.


## 4.2. Forecast and Industrial Structure Analysis in Local GDP Economy

According to the input-output theory and related mathematical models, most countries in the world began to make input-output tables, and countries began to flexibly use the output model to analyze many important issues of national development. Our country is no exception. The input-output table is used to analyze the relationship between different sectors and end-user sectors in our country to understand the state of economic development. Therefore, many professors and researchers have devoted their efforts to describe the application and analysis of the EC + IO model in our country’s economic development. However, since our country does not compile the input-output table every year, we can make statistical improvements to the input-output table for the years that have not been compiled on the original basis. The yearbook data then calculate the overall performance of each department for each year. Compared with previous test results, our test will be more comprehensive, not bound by personal thoughts, and presented to the public more objectively and fairly. From the perspective of the country, it can be viewed more modestly and cautiously, and from the perspective of the troops, it can also be a more objective and fair analysis, which helps to better understand the development of our country. Therefore, the analysis of our country’s input-output table is very important for the country and the masses.From the data in Figure7 and Table 2, we can see that the employment structure of major industries has been declining year by year since 2000, and this trend is relatively obvious and the decline is relatively large. Despite some changes in industrial growth, the overall trend is upward. The employment structure of the service industry has increased year by year, and its proportion in total employment usually exceeds that of major industries, and it is the industry with the largest number of employees. Judging from the development situation at home and abroad, the employment structure of our country’s primary industry is still relatively high, the level of agricultural mechanization is insufficient, the employment of the processing industry is obviously sufficient and relatively insufficient, the industrialization still needs to be improved, and the service scope bears the heavy burden of work and needs to be vigorously developed. It needs to continue to grow, and the entire industry structure also needs to be optimized and improved. From 2016 to 2021, we can see that the GDP growth rate of the region is still relatively high, basically higher than the national growth level. Especially in the past two years, the national growth rate is relatively low, and the GDP growth rate of the region maintains a certain advantage. In the three industrial structures, the proportion of secondary industry is relatively large, and the contribution of growth rate and GDP growth rate is relatively large. In the whole economic structure, different industries have maintained positive growth, so it is necessary to adjust the industrial development strategy for the primary industry and then provide intelligent upgrading services for the primary industry.Figure 7
Three changes in industrial structure.Table 2
Three changes in industrial structure.
Primary industry (%)Secondary industry (%)Tertiary industry (%)GDP growth rate (%)201658.5012.508.4020177.808.2010.50920187.508.209.508.8020196.207.608.507.9020205.106.207.605.6020218.509.6010.209.50From the data in Figure8, we can see that the actual proportion of the first industry is 23%, the proportion of the second industry is 33%, and the proportion of the third industry is 17%. The proportion of the primary industry predicted by the s-svm model is 26.53%, the proportion of the primary industry predicted by the K-means model is 27.38%, and the proportion of the primary industry predicted by the FM model is 28.91%. It can be seen that the prediction value under the s-svm model is the most accurate among the three models.Figure 8
The proportion of industrial structure and its predicted value under the machine learning model.


## 5. Conclusion

The process of regional economic growth is a long-term evolutionary law. During this long evolutionary process, some regions may continue to grow, while others may fall into decline. It takes a long time. For example, from the perspective of our country’s regional economic growth since the turn of the century, the east coast has been in a relatively developed state, while the economy of some western regions is relatively backward. Therefore, how to promote the long-term growth of developed regions and revitalize the troubled regional economy by studying the long-term growth mechanism of the regional economy is an important topic of regional economic research.



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*Source: 7089914-2022-10-06.xml*