# Contribution and Mechanism of Different Levels of Educational Human Capital to the Identification of Regional Green Economic Growth

Authors: Jiapeng Guo
Journal: Computational Intelligence and Neuroscience
 (2021)

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## Abstract

With the rapid development of economy and society and the continuous optimization of industrial structure, the demand for high skilled talents is increasing. Education plays an irreplaceable role in China’s economic growth. Therefore, it is of great significance to study the impact of talent supply of vocational education on economic growth from the perspective of human capital and comprehensively consider the spatial interaction of economic growth. Taking intelligent image recognition technology as the main research technology, this paper discusses the contribution of educational human capital to regional green economic growth. This paper expounds the content of intelligent image recognition technology, constructs an image recognition system based on neural network, and studies the relationship between human resource utilization efficiency and regional economy under intelligent image recognition technology based on the empirical analysis of intelligent image recognition technology. Finally, it makes an empirical study on the spatial effect in the image recognition system and expounds the relationship between feature space and economic growth. It verifies the relationship between educational human capital and green economic growth. The results show that the intelligent image recognition technology has a good effect in the research of spatial effect.


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## Body

## 1. Introduction

In recent years, the contradiction between the coordinated development of China’s vocational education development and economic growth has also appeared, such as the increasing population base, the aggravation of population aging, and the imbalance of population structure. How to improve the level of human capital and create talent dividends, deepen the supply side reform of vocational education, and improve the school running quality of vocational education is of great significance to economic development. It is particularly important for training highly skilled labor force.Theoretical significance is as follows: on the basis of summarizing the impact of the development of vocational education on the level of economic growth, this paper combines the theories of human capital and endogenous economic growth. This paper makes a descriptive analysis from the perspective of human capital, vocational education, and economic development level and analyzes the development trend of education and economic growth from the two aspects of human capital and economy and vocational education and economy. Finally, it makes an empirical test on the impact of vocational education on economic growth from the perspective of China’s human capital, so as to provide a theoretical basis for formulating the policy of coordinated development of China’s vocational education and economy.Practical significance is as follows: at present, China’s population structure is unbalanced, which affects the level of economic development. At this time, the transformation of economic development mode is imminent, especially to create talent dividends by improving the level of human capital and ensure the sustainable and healthy development of the economy. At present, the relevant research on the impact of human capital on economic growth and the impact of vocational education on economic growth is mainly carried out unilaterally. Most of researches use time series data or panel data model, and few comprehensively consider the impact of talent supply of vocational education on economic growth from the perspectives of human capital and empirical analysis. Most studies mainly focus on time series and panel data and rarely consider spatial interaction. However, with the development of economic globalization, human capital externality, and technology spillover, the impact of spatial interaction on economic growth is becoming more and more important. Therefore, image recognition technology can be applied to this kind of research.Taking intelligent image recognition technology as the main research technology, this paper discusses the contribution of educational human capital to regional green economic growth. This paper expounds the content of intelligent image recognition technology, constructs an image recognition system based on neural network, and creatively studies the relationship between human resource utilization efficiency and regional economy under intelligent image recognition technology on the basis of empirical analysis of intelligent image recognition technology. Finally, this paper makes an empirical study on the spatial effect in the image recognition system and expounds the relationship between feature space and economic growth. It verifies the relationship between educational human capital and green economic growth. The results show that the intelligent image recognition technology has a good effect in the research of spatial effect.The article is divided into five parts: The first part is the background introduction and research significance. The second part is the research status at home and abroad. The third part is the introduction of intelligent image recognition technology and related methods. The fourth part is the results and analysis. The fifth part is the summary of the article.


## 2. State of the Art

There is little literature on human capital and vocational education in other countries. Wang and Luo believe that Germany’s high skilled human capital is realized through the dual system. Through the dual system training, students can learn and use flexibly, combine theory with practice, and meet the needs of social and economic development for high skilled talents [1]. Human capital and independent innovation ability play a very important role in economic growth, and human capital has a greater impact on economic growth than independent innovation [2]. Qi et al. believe that the improvement of human capital stock promotes technological progress. In turn, technological progress needs higher human capital to adapt to it, so as to increase human capital investment and form a virtuous circle of economic growth [3]. Purwati et al. used the endogenous economic growth model. The results show that human capital formation is mainly composed of public education investment, emphasizing that the government is the main undertaker of education expenditure, and the government should take necessary financial investment in human capital formation, which is an important factor to promote sustainable economic growth [4]. Al‐Kalouti et al. believe that human capital is the basic driving force of regional economic growth. For underdeveloped region K, increasing human capital investment is conducive to improving regional productivity and shortening the economic gap between other developed regions [5]. Zhang and Merchant believe that the definition of performance in developing countries is flawed and should focus on the impact of economic and human capital in developing countries [6]. Figueiredo et al. divided the impact of human capital on economy into two parts: direct impact and indirect impact [7]. Gloet and Samson believe that American vocational education and training have no effect on improving personal wage income [8]. Surya et al. believe that early vocational education has little or no effect on economic growth [9]. Pradhan et al. believe that education expenditure can significantly promote the economy of developed countries but has no significant impact on the economy of developing countries [10].Other scholars have found that the role of vocational education in promoting economic growth is very obvious. Acosta-Prado et al. found through research that vocational education will bring about more benefits to ordinary families compared to general higher education, and students will get more benefits from strict vocational education. It can be seen that vocational education is worth advocating [11]. Gherghina et al. analyzed 57 research reports by using the method of metaregression analysis to test the effect of education on economic growth. The results show that education has a significant positive correlation with economic growth [12].As regards research on vocational education and economic growth from the perspective of human capital, Xiong et al. divided individual life into three stages by establishing a generational overlapping endogenous growth model, in which human capital studied the impact of the proportion of education expenditure of compulsory education and university education on economic growth by choosing compulsory education and university education. The final result is that when the overall scale of education expenditure is very small, all expenditure should be used for compulsory education. When the education expenditure is greater than a critical value, a certain proportion of education expenditure should be invested in university education, and the investment in university education should increase with the increase of total expenditure [13].Fan et al. believe that the attitude of human capital investors, the differences in the development of market economy, and the income of human capital investment are all important factors affecting the development of vocational education [14]. Vu put forward the importance of the development of vocational education, considered that vocational education is an important part in the process of human capital formation, and defined the talent training objectives of vocational education [15]. Dogbe et al. proposed that the government should attach great importance to the development of vocational education, improve the development of vocational skilled human resources, and emphasize that the talent training objectives of vocational education should meet the current needs of China’s economic and social development [16]. From the perspective of the impact of vocational education on human capital, Sopa et al. believed that vocational education itself has the function of developing human resources and accumulating human capital. However, due to the influence of development stage, system, and other factors, vocational education failed to give full play to its function, affecting the speed and scale of human capital accumulation in China [17]. Ferreira et al. looked at the quality evaluation of vocational education from the perspective of human capital. Whether it has characteristics reflects the particularity of human capital training, and whether it is first class reflects the initial value of human capital [18]. Mendoza-Silva believes that one of the main reasons for the restrictions on the development of vocational education is the lack of human capital investment in vocational education, and the objective existence of human capital investment risk in vocational education is an important reason that directly leads to the reluctance of investors to invest [19]. Peng et al. believe that the role of human capital in production is much greater than that of physical capital, and the proportion of vocational education in the whole education system is gradually increasing, so that society and individuals actively participate in the investment of vocational education [20].For research on the relationship between human capital and economic growth, there is a long-term and stable relationship between human capital, independent innovation, and economic growth. Putra et al. believe that China’s human capital structure has a negative impact on economic development, and there are significant differences in human capital structure in different regions and points [21]. Hansen et al. found that the investment of human capital can promote the improvement of economic growth, but the impact is small. At present, the current impact of human capital and its structure on economic growth is not obvious, there is a lag effect, and the impact of human capital structure on economic growth has obviously regional differences [22]. Oliinyk et al. believe that the coupling degree between human capital level and economic growth in China’s provinces has always been at a low level and is decreasing from east to west. The eastern and northeast regions should improve the ability of human capital innovation, while the central and western regions should improve the ability of human capital to absorb and imitate advanced technology [23]. Lei et al. believe that the direct impact of national human capital on economic growth is not obvious. Human capital mainly acts on economic growth indirectly in the form of technological innovation and imitation. In the eastern region, human capital has a direct and indirect impact on economic development, and the direct impact of human capital on economic development in the central and western regions is not obvious. It mainly indirectly affects economic development through technological innovation [24]. Shao believes that there is conditional convergence in the economic growth of all provinces in China, and the convergence rate reaches 2.4%, which shows that the economies of all provinces are more and more closely related, and the level of human capital has a significant role in promoting economic growth [25]. For research on the relationship between vocational education and economic growth, some scholars in other countries believe that it is not obvious. From the above literature review, it can be found that spatial effect is rarely used to study the relationship between educational human capital and economic growth. Therefore, a new intelligent image recognition technology is used to study the contribution of educational human capital promotion area to green economic growth.


## 3. Methodology

### 3.1. Intelligent Image Recognition Technology

A formula is used to explain the multioutput classification of intelligent image recognition. Suppose that the output spaceΩ=λ1,λ2,…,λm is a set of m-dimensional output variables, in which Φλi in λi has different values. Multioutput classifier h is the value y=y1,y2,…,ym of learning input x on m-dimensional output variable, as shown in the following formulas:(1)h:ΦX⟶Φλ1×Φλ2×⋯×Φλm,(2)x⟶y1,y2,…,ym,where yi∈Φλi=0,1,…,ki−1 and ΦX and Φλii=1,2,…,m represent the value spaces of input variable and output variable, respectively.ResNet is shown in Figure1 and DenseNet is shown in Figure 2. This part of DenseNet borrows the basic idea of ResNet. However, it establishes a close connection between the current layer and all previous layers and realizes feature reuse through the direct connection of features on the channel. Compared with ResNet’s direct feature addition, DenseNet splices features to expand the data dimension. These features make DenseNet have obvious advantages over other networks.Figure 1
ResNet.Figure 2
DenseNet.Different from ResNet, DenseNet proposes a dense connection mechanism: all layers in the network are interconnected; that is, each layer will receive the output results from all previous layers as input. Moreover, DenseNet directly concatenates feature maps from different layers, which can realize feature reuse and improve efficiency, which is also the main difference between DenseNet and ResNet, as shown in the following formulas:(3)xl=Hlxl−1,(4)xl=Hlxl−1+xl−1,(5)xl=Hlx0,x1,…,xl−1,where Hl⋅ represents a nonlinear function. In the formula, the first represents the output of the traditional network on the layer, the second represents the output on the ResNet network, and the last represents the output on the DenseNet network.The dense block structure of DenseNet core is shown in Figure3. As can be seen from the figure, through the deny block module, the input of each layer comes from the output of all previous layers. Therefore, DenseNet realizes the purposes of reducing gradient disappearance, feature reuse, and reducing the amount of parameters and further improves the feature extraction ability of the model.Figure 3
DenseNet structure diagram.


### 3.2. Construction of Image Recognition System Based on Neural Network

The main basis of image recognition system is neural network, as shown in Figure4. The image recognition system must have two working modes. One of them is the training mode. In this working state, the teacher uses the sample image to train the system. For each sample, the teacher gives a corresponding classification until the system can correctly recognize all samples.Figure 4
Image recognition system training method based on neural network.An image can be represented by a two-dimensional function:(6)I=fx,y.The coordinates ofx,y point on the image plane. Each point has a corresponding brightness value I. When x,y, and I are continuous values, the image is an analog image. On the contrary, when x,y, and I go to a series of discrete values, the image is a digital image. In the natural state, the light reflected by the object forms an inverted image in the focal plane through a convex lens, which is Fourier transform. Fourier series, the predecessor of Fourier transform, was proposed by French mathematician Fourier. In his book thermal analysis theory published in 1822, he pointed out that any periodic function can be expressed as sine or cosine functions and forms of different frequencies. The Fourier series formula of function fx with period T is(7)fx=a02+∑n=1∞ancos2nπTx+bnsin2nπTx,an=2T∫−T/2T/2fxcos2nπTxdx,bn=2T∫−T/2T/2fxsin2nπTxdx.The original function is decomposed into the sum of a set of sine and cosine components (the DC componenta0 can be regarded as the cosine component with infinite period). The frequency of each sine and cosine component is an integral multiple of the frequency of the function fx, and the two different components are orthogonal to each other; that is,(8)∫−T/2T/2cos2mπTxcos2nπTxdx=∫−T/2T/2sin2mπTxsin2nπTxdx=0,m≠n,T,m=n,∫−T/2T/2sin2mπTxcos2nπTxdx=0.Because the sine function and cosine function of the same period only differ byπ/2 in phase, the cosine function can also be represented by sine function, and vice versa. Therefore, we can collectively call them sine function family.After the model training, the vehicle images extracted from the video are classified using the model. Firstly, the vehicle image is input into the trained residual network model, and the image feature is extracted through the model. After the classifier classifies the input feature vector, the results are displayed in the client interface, and the vehicle information is identified and saved in the database. The process is shown in Figure5.Figure 5
Image classification flowchart.The spatial Dobbin panel model of the impact of vocational education on the level of economic development from the perspective of human capital constructed by the image recognition system based on neural network is as follows:(9)lnpgdpit=α+ρ∑i=131Wijlnpgdpit+β1lnKit+β2lnLit+β3lnSVEit+β4lnHVEit+β5lnEit+γ1∑i=131WijlnKit+γ2∑i=131WijlnLit+γ3∑i=131WijlnSVEit+γ4∑i=131WijlnHVEit+γ5∑i=131WijlnEit+εit.In the above formula,γ1,γ2,γ3,γ4,γ5 represent the spatial autocorrelation coefficient of total capital formation, number of employed persons, number of secondary vocational education graduates, number of higher vocational education graduates, and per capita years of education.After obtaining the spatial weight, we should further investigate whether the index data has spatial dependence. Only when there is spatial dependence can the spatial econometric model be used. Spatial autocorrelation is divided into global spatial autocorrelation and local spatial autocorrelation. In this paper, the most commonly used spatial autocorrelation index Moran’sI (Moran index I) is selected. Spatial autocorrelation index is as follows:(10)I=∑i=1n∑j=1nWijxi−x¯xj−x¯S2∑i=1n∑j=1nWij,where S2=∑i=1nxi−x¯2/n is the sample variance, wij is the i,j element of the spatial weight matrix, and ∑i=1n∑j=1nWij is the sum of all spatial weights. The spatial weight matrix is standardized, and Moran’s I is(11)I=∑i=1n∑j=1nWijxi−x¯xj−x¯S2∑i=1nxi−x¯2.The value range of Moran indexI is −1,1, I>0 indicates positive autocorrelation, and I<0 indicates negative correlation.The local Moran indexI is used to observe the spatial agglomeration of a region, and its formula is(12)I=x−ix¯S2∑j=1nwijxj−x¯.Among them, the global autocorrelation is a value, while the local autocorrelation is the correlation value of each region. Four statistically significant high, high, low, and low and high regions are obtained. The scatter diagram of the local autocorrelation of each region is fitted with a linear line, and the slope is the global autocorrelation. When the local Moran index is positive, it indicates that the high (low) value of regioni is surrounded by the surrounding high (low) value; when index I is negative, it indicates that the high (low) value of region i is surrounded by the surrounding low (high) value.


## 3.1. Intelligent Image Recognition Technology

A formula is used to explain the multioutput classification of intelligent image recognition. Suppose that the output spaceΩ=λ1,λ2,…,λm is a set of m-dimensional output variables, in which Φλi in λi has different values. Multioutput classifier h is the value y=y1,y2,…,ym of learning input x on m-dimensional output variable, as shown in the following formulas:(1)h:ΦX⟶Φλ1×Φλ2×⋯×Φλm,(2)x⟶y1,y2,…,ym,where yi∈Φλi=0,1,…,ki−1 and ΦX and Φλii=1,2,…,m represent the value spaces of input variable and output variable, respectively.ResNet is shown in Figure1 and DenseNet is shown in Figure 2. This part of DenseNet borrows the basic idea of ResNet. However, it establishes a close connection between the current layer and all previous layers and realizes feature reuse through the direct connection of features on the channel. Compared with ResNet’s direct feature addition, DenseNet splices features to expand the data dimension. These features make DenseNet have obvious advantages over other networks.Figure 1
ResNet.Figure 2
DenseNet.Different from ResNet, DenseNet proposes a dense connection mechanism: all layers in the network are interconnected; that is, each layer will receive the output results from all previous layers as input. Moreover, DenseNet directly concatenates feature maps from different layers, which can realize feature reuse and improve efficiency, which is also the main difference between DenseNet and ResNet, as shown in the following formulas:(3)xl=Hlxl−1,(4)xl=Hlxl−1+xl−1,(5)xl=Hlx0,x1,…,xl−1,where Hl⋅ represents a nonlinear function. In the formula, the first represents the output of the traditional network on the layer, the second represents the output on the ResNet network, and the last represents the output on the DenseNet network.The dense block structure of DenseNet core is shown in Figure3. As can be seen from the figure, through the deny block module, the input of each layer comes from the output of all previous layers. Therefore, DenseNet realizes the purposes of reducing gradient disappearance, feature reuse, and reducing the amount of parameters and further improves the feature extraction ability of the model.Figure 3
DenseNet structure diagram.


## 3.2. Construction of Image Recognition System Based on Neural Network

The main basis of image recognition system is neural network, as shown in Figure4. The image recognition system must have two working modes. One of them is the training mode. In this working state, the teacher uses the sample image to train the system. For each sample, the teacher gives a corresponding classification until the system can correctly recognize all samples.Figure 4
Image recognition system training method based on neural network.An image can be represented by a two-dimensional function:(6)I=fx,y.The coordinates ofx,y point on the image plane. Each point has a corresponding brightness value I. When x,y, and I are continuous values, the image is an analog image. On the contrary, when x,y, and I go to a series of discrete values, the image is a digital image. In the natural state, the light reflected by the object forms an inverted image in the focal plane through a convex lens, which is Fourier transform. Fourier series, the predecessor of Fourier transform, was proposed by French mathematician Fourier. In his book thermal analysis theory published in 1822, he pointed out that any periodic function can be expressed as sine or cosine functions and forms of different frequencies. The Fourier series formula of function fx with period T is(7)fx=a02+∑n=1∞ancos2nπTx+bnsin2nπTx,an=2T∫−T/2T/2fxcos2nπTxdx,bn=2T∫−T/2T/2fxsin2nπTxdx.The original function is decomposed into the sum of a set of sine and cosine components (the DC componenta0 can be regarded as the cosine component with infinite period). The frequency of each sine and cosine component is an integral multiple of the frequency of the function fx, and the two different components are orthogonal to each other; that is,(8)∫−T/2T/2cos2mπTxcos2nπTxdx=∫−T/2T/2sin2mπTxsin2nπTxdx=0,m≠n,T,m=n,∫−T/2T/2sin2mπTxcos2nπTxdx=0.Because the sine function and cosine function of the same period only differ byπ/2 in phase, the cosine function can also be represented by sine function, and vice versa. Therefore, we can collectively call them sine function family.After the model training, the vehicle images extracted from the video are classified using the model. Firstly, the vehicle image is input into the trained residual network model, and the image feature is extracted through the model. After the classifier classifies the input feature vector, the results are displayed in the client interface, and the vehicle information is identified and saved in the database. The process is shown in Figure5.Figure 5
Image classification flowchart.The spatial Dobbin panel model of the impact of vocational education on the level of economic development from the perspective of human capital constructed by the image recognition system based on neural network is as follows:(9)lnpgdpit=α+ρ∑i=131Wijlnpgdpit+β1lnKit+β2lnLit+β3lnSVEit+β4lnHVEit+β5lnEit+γ1∑i=131WijlnKit+γ2∑i=131WijlnLit+γ3∑i=131WijlnSVEit+γ4∑i=131WijlnHVEit+γ5∑i=131WijlnEit+εit.In the above formula,γ1,γ2,γ3,γ4,γ5 represent the spatial autocorrelation coefficient of total capital formation, number of employed persons, number of secondary vocational education graduates, number of higher vocational education graduates, and per capita years of education.After obtaining the spatial weight, we should further investigate whether the index data has spatial dependence. Only when there is spatial dependence can the spatial econometric model be used. Spatial autocorrelation is divided into global spatial autocorrelation and local spatial autocorrelation. In this paper, the most commonly used spatial autocorrelation index Moran’sI (Moran index I) is selected. Spatial autocorrelation index is as follows:(10)I=∑i=1n∑j=1nWijxi−x¯xj−x¯S2∑i=1n∑j=1nWij,where S2=∑i=1nxi−x¯2/n is the sample variance, wij is the i,j element of the spatial weight matrix, and ∑i=1n∑j=1nWij is the sum of all spatial weights. The spatial weight matrix is standardized, and Moran’s I is(11)I=∑i=1n∑j=1nWijxi−x¯xj−x¯S2∑i=1nxi−x¯2.The value range of Moran indexI is −1,1, I>0 indicates positive autocorrelation, and I<0 indicates negative correlation.The local Moran indexI is used to observe the spatial agglomeration of a region, and its formula is(12)I=x−ix¯S2∑j=1nwijxj−x¯.Among them, the global autocorrelation is a value, while the local autocorrelation is the correlation value of each region. Four statistically significant high, high, low, and low and high regions are obtained. The scatter diagram of the local autocorrelation of each region is fitted with a linear line, and the slope is the global autocorrelation. When the local Moran index is positive, it indicates that the high (low) value of regioni is surrounded by the surrounding high (low) value; when index I is negative, it indicates that the high (low) value of region i is surrounded by the surrounding low (high) value.


## 4. Result Analysis and Discussion

### 4.1. Empirical Analysis Based on Intelligent Image Recognition Technology

#### 4.1.1. Research on the Relationship between Human Resource Utilization Efficiency and Regional Economy under Intelligent Image Recognition Technology

Deep convolution neural network (CNN) algorithm is a widely used neural network construction model in the field of deep learning. Caffe is the fastest CNN architecture at present. In this paper, MPI technology is used for data parallel optimization to retain the characteristics of the original Caffe architecture. Local spatial autocorrelation tests the overall spatial dependence of economic growth indicators and further analyzes the dispersion of various provinces and cities through local spatial autocorrelation test. Spatial clustering scattergram represents the relationship between index variables and spatial lag vector through two-dimensional rectangular coordinate system and can judge the spatial aggregation type of provinces and cities. In this paper, GeoDa1.6 software is used to calculate the local Moran’sI index. Figure 6 shows the local Moran’s I scatter diagram in 2010 and 2020. The data sets selected in this paper are human resources indicators, regional economic growth indicators, total capital formation, number of employed persons, number of secondary vocational education graduates in 31 provinces, cities, and autonomous regions in China in 2010 and 2020, the number of graduates of higher vocational education, and the number of years of education per capita.Figure 6
2010 and 2020 spatial clustering scatter plot.According to the analysis of Figure7, the ratio of human resource utilization efficiency to material capital utilization efficiency shows an upward trend over time; that is, the utilization efficiency of human resources continues to improve. The greater the ratio of human resources stock to material capital is, that is, the higher the marginal output benefit of human resources is than that of material capital. We should increase the investment in human resources. For human resources, the same investment produces much better economic benefits than material capital. In the era of knowledge economy, the development and utilization of human resources are becoming more and more important, and their role in economic development is becoming more and more important. Only by strengthening the rational development and effective management of human resources can we promote the efficient and healthy development of regional economy.Figure 7
The ratio of human resource stock to physical capital utilization efficiency.


### 4.2. An Empirical Study on Spatial Effect in Image Recognition System

#### 4.2.1. Research on the Relationship between Characteristic Space and Economic Growth

After that, a simple comparison is made on the trend of economic error. Through Figure8, it can be found that the prediction accuracy without image recognition technology is not stable because of the lack of research on spatial effect. However, after adding the research on spatial effect through intelligent image recognition technology, the convergence speed is significantly improved. Therefore, we can see the effectiveness of intelligent image recognition technology.Figure 8
Trend change graph of economic error without using image recognition technology.Using intelligent image recognition technology, the proposed method also uses the feature vector output from the bottleneck layer as the input of domain discriminator, the calculation of cluster center, and the construction of cluster center graph. For the domain discriminator, we use the combination of three full connection layers and processing layers. The dimension from input to output is in the order of256⟶1024⟶1024⟶1. During training, the model updates the weights of all layers of the domain discriminator, the bottleneck layer of the feature extractor, and all layers in the classifier and fine-tunes the weights of the network layer before the neck layer in the bottle feature generator. The results are shown in Figure 9.Figure 9
The spatial distribution of economic features and the distribution of clustering centers after processing by intelligent image recognition technology.The graph structure information obtained from the center of each category in the feature space can be cross domain invariant information and can be used as a guiding model for cross domain migration learning. The relationship between cluster centers can be used as a potential representation to provide meaningful information for knowledge transfer. We expect that, in the process of domain adaptation, the relative position of each class in the embedded manifold can be consistent as a whole, so as to facilitate the adaptation of the classifier. Figure10 shows the changes of economic prediction accuracy in different feature spaces. It can be seen that the economic prediction accuracy has been greatly improved after processing by intelligent image recognition technology.Figure 10
Changes in the accuracy of economic predictions in different feature spaces after processing by intelligent image recognition technology.


#### 4.2.2. Verification of the Relationship between Educational Human Capital and Green Economic Growth

In order to study the relationship between educational human capital region and economic growth, as shown in Figure11, different numbers of intermediate states can be introduced by setting independent spatial characteristics. The application of intelligent image recognition technology requires scheduling space, which can be expressed by the number of network states. For example, when the number of network states is 6, the maximum network congestion J introduced is 0.11.Figure 11
The effect of different parameters on the results under intelligent image recognition technology.The contribution of educational human capital to economic growth can be demonstrated through data flow. With the increase of the number of flows, MDVP can greatly reduce the number of network states compared with Swan. Specifically, when the number of flows in the network is 3000, Swan needs to introduce 18 network states to get the update plan. In contrast, MDVP only introduces 9 network states, which greatly reduces the update time. Both “a” and “b” in Figure12 show a similar trend; that is, when the number of streams increases, the gap between Swan and MDVP will become larger. It is worth mentioning that the one-shot method is always composed of two network states, namely, the initial state and the final state.Figure 12
Different data flow under intelligent image recognition technology.As shown in Figure13, the average value of the total demand violation after processing by the intelligent image recognition technology is 251. In contrast, the average value of the total demand violation of the MCUP is 343, which is about 37% higher than the algorithm proposed in this chapter. This is because MCUP only focuses on the maximum network congestion in the network and ignores the demand violation of the restricted flow in the intermediate state. It can be found that the intervention of educational human capital makes a great contribution to the growth of green economy in a region.Figure 13
The sum of all demand violation rates after the intervention of educational capital.


## 4.1. Empirical Analysis Based on Intelligent Image Recognition Technology

### 4.1.1. Research on the Relationship between Human Resource Utilization Efficiency and Regional Economy under Intelligent Image Recognition Technology

Deep convolution neural network (CNN) algorithm is a widely used neural network construction model in the field of deep learning. Caffe is the fastest CNN architecture at present. In this paper, MPI technology is used for data parallel optimization to retain the characteristics of the original Caffe architecture. Local spatial autocorrelation tests the overall spatial dependence of economic growth indicators and further analyzes the dispersion of various provinces and cities through local spatial autocorrelation test. Spatial clustering scattergram represents the relationship between index variables and spatial lag vector through two-dimensional rectangular coordinate system and can judge the spatial aggregation type of provinces and cities. In this paper, GeoDa1.6 software is used to calculate the local Moran’sI index. Figure 6 shows the local Moran’s I scatter diagram in 2010 and 2020. The data sets selected in this paper are human resources indicators, regional economic growth indicators, total capital formation, number of employed persons, number of secondary vocational education graduates in 31 provinces, cities, and autonomous regions in China in 2010 and 2020, the number of graduates of higher vocational education, and the number of years of education per capita.Figure 6
2010 and 2020 spatial clustering scatter plot.According to the analysis of Figure7, the ratio of human resource utilization efficiency to material capital utilization efficiency shows an upward trend over time; that is, the utilization efficiency of human resources continues to improve. The greater the ratio of human resources stock to material capital is, that is, the higher the marginal output benefit of human resources is than that of material capital. We should increase the investment in human resources. For human resources, the same investment produces much better economic benefits than material capital. In the era of knowledge economy, the development and utilization of human resources are becoming more and more important, and their role in economic development is becoming more and more important. Only by strengthening the rational development and effective management of human resources can we promote the efficient and healthy development of regional economy.Figure 7
The ratio of human resource stock to physical capital utilization efficiency.


## 4.1.1. Research on the Relationship between Human Resource Utilization Efficiency and Regional Economy under Intelligent Image Recognition Technology

Deep convolution neural network (CNN) algorithm is a widely used neural network construction model in the field of deep learning. Caffe is the fastest CNN architecture at present. In this paper, MPI technology is used for data parallel optimization to retain the characteristics of the original Caffe architecture. Local spatial autocorrelation tests the overall spatial dependence of economic growth indicators and further analyzes the dispersion of various provinces and cities through local spatial autocorrelation test. Spatial clustering scattergram represents the relationship between index variables and spatial lag vector through two-dimensional rectangular coordinate system and can judge the spatial aggregation type of provinces and cities. In this paper, GeoDa1.6 software is used to calculate the local Moran’sI index. Figure 6 shows the local Moran’s I scatter diagram in 2010 and 2020. The data sets selected in this paper are human resources indicators, regional economic growth indicators, total capital formation, number of employed persons, number of secondary vocational education graduates in 31 provinces, cities, and autonomous regions in China in 2010 and 2020, the number of graduates of higher vocational education, and the number of years of education per capita.Figure 6
2010 and 2020 spatial clustering scatter plot.According to the analysis of Figure7, the ratio of human resource utilization efficiency to material capital utilization efficiency shows an upward trend over time; that is, the utilization efficiency of human resources continues to improve. The greater the ratio of human resources stock to material capital is, that is, the higher the marginal output benefit of human resources is than that of material capital. We should increase the investment in human resources. For human resources, the same investment produces much better economic benefits than material capital. In the era of knowledge economy, the development and utilization of human resources are becoming more and more important, and their role in economic development is becoming more and more important. Only by strengthening the rational development and effective management of human resources can we promote the efficient and healthy development of regional economy.Figure 7
The ratio of human resource stock to physical capital utilization efficiency.


## 4.2. An Empirical Study on Spatial Effect in Image Recognition System

### 4.2.1. Research on the Relationship between Characteristic Space and Economic Growth

After that, a simple comparison is made on the trend of economic error. Through Figure8, it can be found that the prediction accuracy without image recognition technology is not stable because of the lack of research on spatial effect. However, after adding the research on spatial effect through intelligent image recognition technology, the convergence speed is significantly improved. Therefore, we can see the effectiveness of intelligent image recognition technology.Figure 8
Trend change graph of economic error without using image recognition technology.Using intelligent image recognition technology, the proposed method also uses the feature vector output from the bottleneck layer as the input of domain discriminator, the calculation of cluster center, and the construction of cluster center graph. For the domain discriminator, we use the combination of three full connection layers and processing layers. The dimension from input to output is in the order of256⟶1024⟶1024⟶1. During training, the model updates the weights of all layers of the domain discriminator, the bottleneck layer of the feature extractor, and all layers in the classifier and fine-tunes the weights of the network layer before the neck layer in the bottle feature generator. The results are shown in Figure 9.Figure 9
The spatial distribution of economic features and the distribution of clustering centers after processing by intelligent image recognition technology.The graph structure information obtained from the center of each category in the feature space can be cross domain invariant information and can be used as a guiding model for cross domain migration learning. The relationship between cluster centers can be used as a potential representation to provide meaningful information for knowledge transfer. We expect that, in the process of domain adaptation, the relative position of each class in the embedded manifold can be consistent as a whole, so as to facilitate the adaptation of the classifier. Figure10 shows the changes of economic prediction accuracy in different feature spaces. It can be seen that the economic prediction accuracy has been greatly improved after processing by intelligent image recognition technology.Figure 10
Changes in the accuracy of economic predictions in different feature spaces after processing by intelligent image recognition technology.


### 4.2.2. Verification of the Relationship between Educational Human Capital and Green Economic Growth

In order to study the relationship between educational human capital region and economic growth, as shown in Figure11, different numbers of intermediate states can be introduced by setting independent spatial characteristics. The application of intelligent image recognition technology requires scheduling space, which can be expressed by the number of network states. For example, when the number of network states is 6, the maximum network congestion J introduced is 0.11.Figure 11
The effect of different parameters on the results under intelligent image recognition technology.The contribution of educational human capital to economic growth can be demonstrated through data flow. With the increase of the number of flows, MDVP can greatly reduce the number of network states compared with Swan. Specifically, when the number of flows in the network is 3000, Swan needs to introduce 18 network states to get the update plan. In contrast, MDVP only introduces 9 network states, which greatly reduces the update time. Both “a” and “b” in Figure12 show a similar trend; that is, when the number of streams increases, the gap between Swan and MDVP will become larger. It is worth mentioning that the one-shot method is always composed of two network states, namely, the initial state and the final state.Figure 12
Different data flow under intelligent image recognition technology.As shown in Figure13, the average value of the total demand violation after processing by the intelligent image recognition technology is 251. In contrast, the average value of the total demand violation of the MCUP is 343, which is about 37% higher than the algorithm proposed in this chapter. This is because MCUP only focuses on the maximum network congestion in the network and ignores the demand violation of the restricted flow in the intermediate state. It can be found that the intervention of educational human capital makes a great contribution to the growth of green economy in a region.Figure 13
The sum of all demand violation rates after the intervention of educational capital.


## 4.2.1. Research on the Relationship between Characteristic Space and Economic Growth

After that, a simple comparison is made on the trend of economic error. Through Figure8, it can be found that the prediction accuracy without image recognition technology is not stable because of the lack of research on spatial effect. However, after adding the research on spatial effect through intelligent image recognition technology, the convergence speed is significantly improved. Therefore, we can see the effectiveness of intelligent image recognition technology.Figure 8
Trend change graph of economic error without using image recognition technology.Using intelligent image recognition technology, the proposed method also uses the feature vector output from the bottleneck layer as the input of domain discriminator, the calculation of cluster center, and the construction of cluster center graph. For the domain discriminator, we use the combination of three full connection layers and processing layers. The dimension from input to output is in the order of256⟶1024⟶1024⟶1. During training, the model updates the weights of all layers of the domain discriminator, the bottleneck layer of the feature extractor, and all layers in the classifier and fine-tunes the weights of the network layer before the neck layer in the bottle feature generator. The results are shown in Figure 9.Figure 9
The spatial distribution of economic features and the distribution of clustering centers after processing by intelligent image recognition technology.The graph structure information obtained from the center of each category in the feature space can be cross domain invariant information and can be used as a guiding model for cross domain migration learning. The relationship between cluster centers can be used as a potential representation to provide meaningful information for knowledge transfer. We expect that, in the process of domain adaptation, the relative position of each class in the embedded manifold can be consistent as a whole, so as to facilitate the adaptation of the classifier. Figure10 shows the changes of economic prediction accuracy in different feature spaces. It can be seen that the economic prediction accuracy has been greatly improved after processing by intelligent image recognition technology.Figure 10
Changes in the accuracy of economic predictions in different feature spaces after processing by intelligent image recognition technology.


## 4.2.2. Verification of the Relationship between Educational Human Capital and Green Economic Growth

In order to study the relationship between educational human capital region and economic growth, as shown in Figure11, different numbers of intermediate states can be introduced by setting independent spatial characteristics. The application of intelligent image recognition technology requires scheduling space, which can be expressed by the number of network states. For example, when the number of network states is 6, the maximum network congestion J introduced is 0.11.Figure 11
The effect of different parameters on the results under intelligent image recognition technology.The contribution of educational human capital to economic growth can be demonstrated through data flow. With the increase of the number of flows, MDVP can greatly reduce the number of network states compared with Swan. Specifically, when the number of flows in the network is 3000, Swan needs to introduce 18 network states to get the update plan. In contrast, MDVP only introduces 9 network states, which greatly reduces the update time. Both “a” and “b” in Figure12 show a similar trend; that is, when the number of streams increases, the gap between Swan and MDVP will become larger. It is worth mentioning that the one-shot method is always composed of two network states, namely, the initial state and the final state.Figure 12
Different data flow under intelligent image recognition technology.As shown in Figure13, the average value of the total demand violation after processing by the intelligent image recognition technology is 251. In contrast, the average value of the total demand violation of the MCUP is 343, which is about 37% higher than the algorithm proposed in this chapter. This is because MCUP only focuses on the maximum network congestion in the network and ignores the demand violation of the restricted flow in the intermediate state. It can be found that the intervention of educational human capital makes a great contribution to the growth of green economy in a region.Figure 13
The sum of all demand violation rates after the intervention of educational capital.


## 5. Conclusion

Economic growth is inseparable from a certain quantity and quality of human capital stock. Vocational education is an important way to form human capital accumulation and an important part of China’s education system. Taking intelligent image recognition technology as the main research technology, this paper discusses the contribution of educational human capital to regional green economic growth. The clustering between different regions is studied by autocorrelation test, and then the ratio of human resource stock to material capital utilization efficiency is studied. The research shows that intelligent image recognition technology has a good effect in studying the impact of education human capital promotion on economic growth. Then, by analyzing the spatial effect of intelligent image recognition technology and studying the change of the sum of relevant data flow and demand default rate after the intervention of educational capital, it can be found that the increase of educational capital has a great impact on the economic growth of a region. Only by strengthening the rational development and effective management of human resources can we promote the efficient and healthy development of regional economy. However, the research of this paper lacks algorithm simulation, so it needs to be improved in future research.



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