# Socioeconomic Development Trend Prediction Model and Research Which is Based on Deep Neural Network

Authors: Hongming Zhang; Wenmiao Wei; Kai Wang
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

In recent years, my country’s economic development has entered a new stage, and economic development has entered a new height. Gross Domestic Product (GDP) is the most important and commonly used measure to evaluate social and economic development. It can comprehensively reflect the operating conditions of the economy. At the 2014 APEC summit, Xi Jinping focused on the characteristics of the new normal of China’s economy. The economic growth has undergone major changes, and the economic structure has become more and more complete and has been continuously optimized. Therefore, the driving force of innovation plays an important role in the future social and economic development. The high-tech demonstration zone is the main hub of my country’s economic innovation and scientific and technological progress, and the development of the high-tech demonstration zone is particularly important in the development of the innovative economy. By predicting the economic growth, the development structure can be adjusted in time, and the coordinated allocation of resources can be achieved, which is conducive to the stable and rapid development of the economy. Therefore, it is very necessary to predict my country’s economic growth. At present, more and more scholars have invested more energy in the establishment of economic forecasting models. Previously, the prediction effect of traditional models was not satisfactory. At present, the field of target camera machine learning has been transferred. The application of the powerful nonlinear fitting ability of the machine learning model can improve the prediction effect. Based on this, this paper studies the use of artificial neural network with improved BP algorithm to predict social and economic development, and analyzes social and economic development trends and changing laws. The results show that the forecasting model constructed in this paper has a good forecasting effect, and the research results can provide a theoretical basis for scientifically formulating macro-control policies.


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## Body

## 1. Introduction

There is a correlation between the country’s macroeconomic adjustment and the investment decisions of enterprises and the GDP growth rate. The GDP growth rate forecast has always been the main indicator for evaluating economics. However, the current domestic and foreign forms are becoming more and more complex, which will make it more difficult to predict GDP, which puts forward higher requirements for the designation of economic macropolicies. Therefore, an effective forecasting method should be selected to predict the international economic environment and domestic economic results. This study builds a social and economic development trend prediction model based on a deep neural network, hoping to speed up the speed of social and economic development. The report is as follows.Data show that the GDP value has also shown a substantial increase, which is now analyzed in detail. According to preliminary calculations, the GDP value in 2021 will be 114,367 billion yuan, a year-on-year increase of 8.1% compared with the previous year, as shown in Figure1.Figure 1
GDP and growth rate, 2017–2021.


### 1.1. 2017–2021 GDP and Growth Rate

### 1.2. 2017–2021: The Third Industrial Growth Value Accounted for the Proportion of GDP

The GDP values of primary, secondary, and tertiary industries increased by 8,308.6 billion yuan, 450,904 billion yuan, and 60,968 billion yuan, respectively, with growth rates of 7.1%, 8.2%, and 8.2%, respectively, as shown in Figure2.Figure 2
Growth value of the third sector as a share of GDP, 2017–2021.


### 1.3. Analysis of Monthly Increase in Consumer Prices from January to December 2021

There is a significant increase in the annual consumer price ratio in 2021 compared to 2020, with an increase of about 0.9%, as shown in Figure3.Figure 3
Monthly increase in consumer prices, January–December 2021.GDP forecast is essentially a forecast of GDP time series, which is essentially a set of data that will be influenced by the preceding data, and this influence relationship is mainly expressed as a certain cycle change or trend change, which is generally nonlinear, so it is difficult to establish a fixed and quantitative mathematical relationship formula. The current research methods for economic development trend prediction are mainly divided into linear prediction and nonlinear prediction, such as neural network and gray prediction methods. The economic system is an extremely complex system with time-varying, nonlinear, and uncertain relationships. However, the traditional economic models constructed using econometrics are mostly linear models. These models have difficulty in reflecting the nonlinear relationships in the economic system, and therefore can lead to large errors in the prediction results in economic forecasting. In addition, traditional habits, economic development level, economic operation mechanism, and other factors will also affect the accuracy of this assessment method, so there is a need for more effective forecasting methods to be constructed. Artificial neural network (ANN) is a computing technology established by using human brain simulation. Based on the understanding of some brain functions, computer scientists and mathematicians try to represent the working process of the brain in a mathematical way. Because ANN can simulate the human brain’s thinking process and the parallel storage, transmission and processing of information between neurons in a highly abstract way, with parallelism, stability, high nonlinearity, self-learning, self-organization, and associative memory, ANN models are highly valuable and can play an advantageous role in predicting socioeconomic development, which is difficult to be reflected by traditional prediction models. The problem of large errors in prediction results is due to nonlinear relationships in economic systems. Based on this, this paper studies the use of the artificial neural network with improved BP algorithm to predict socioeconomic development (with GDP as the main measure, i.e., GDP as the explanatory variable) and analyze the socioeconomic development trend and change pattern, and the research results can provide theoretical basis for scientific formulation of macro-control policies.


## 1.1. 2017–2021 GDP and Growth Rate

## 1.2. 2017–2021: The Third Industrial Growth Value Accounted for the Proportion of GDP

The GDP values of primary, secondary, and tertiary industries increased by 8,308.6 billion yuan, 450,904 billion yuan, and 60,968 billion yuan, respectively, with growth rates of 7.1%, 8.2%, and 8.2%, respectively, as shown in Figure2.Figure 2
Growth value of the third sector as a share of GDP, 2017–2021.


## 1.3. Analysis of Monthly Increase in Consumer Prices from January to December 2021

There is a significant increase in the annual consumer price ratio in 2021 compared to 2020, with an increase of about 0.9%, as shown in Figure3.Figure 3
Monthly increase in consumer prices, January–December 2021.GDP forecast is essentially a forecast of GDP time series, which is essentially a set of data that will be influenced by the preceding data, and this influence relationship is mainly expressed as a certain cycle change or trend change, which is generally nonlinear, so it is difficult to establish a fixed and quantitative mathematical relationship formula. The current research methods for economic development trend prediction are mainly divided into linear prediction and nonlinear prediction, such as neural network and gray prediction methods. The economic system is an extremely complex system with time-varying, nonlinear, and uncertain relationships. However, the traditional economic models constructed using econometrics are mostly linear models. These models have difficulty in reflecting the nonlinear relationships in the economic system, and therefore can lead to large errors in the prediction results in economic forecasting. In addition, traditional habits, economic development level, economic operation mechanism, and other factors will also affect the accuracy of this assessment method, so there is a need for more effective forecasting methods to be constructed. Artificial neural network (ANN) is a computing technology established by using human brain simulation. Based on the understanding of some brain functions, computer scientists and mathematicians try to represent the working process of the brain in a mathematical way. Because ANN can simulate the human brain’s thinking process and the parallel storage, transmission and processing of information between neurons in a highly abstract way, with parallelism, stability, high nonlinearity, self-learning, self-organization, and associative memory, ANN models are highly valuable and can play an advantageous role in predicting socioeconomic development, which is difficult to be reflected by traditional prediction models. The problem of large errors in prediction results is due to nonlinear relationships in economic systems. Based on this, this paper studies the use of the artificial neural network with improved BP algorithm to predict socioeconomic development (with GDP as the main measure, i.e., GDP as the explanatory variable) and analyze the socioeconomic development trend and change pattern, and the research results can provide theoretical basis for scientific formulation of macro-control policies.


## 2. Related Research

Since McCulloch and Pitts proposed the MP neural network model, ANN has achieved certain research results in theory, technology, and application, and the application scope has been expanded and has been widely used in information science, psychological science, computer science, and management science. Artificial neural networks have been widely used in many fields [1]; for example, Jiang and Wang [2] built a GDP prediction model based on the BP neural network and predicted China’s 2009 GDP data, and the results showed that the prediction results were more accurate; Lin and Peng [3] designed and built a prediction model based on neural network integration, and took the macroeconomic data of Jiangmen City, Guangdong Province as an example to predict the region’s GDP forecasting, and the results show that this forecasting model can obtain more reliable results. Zhao et al. [4] also completed a prediction of GDP based on a BP neural network model, and the prediction results were better than the model constructed by Lin Jian. Wu et al. [5] used the genetic algorithm to optimize the BP artificial neural network to construct a model for GDP data analysis and used the constructed model for prediction, and the validation results showed that the accuracy of this model was higher [6]. However, it is known from these studies that although the network learning process can be ensured to eventually converge, the traditional BP algorithm still has some significant drawbacks: (1) it tends to fall into local minima and therefore perhaps cannot obtain the overall optimal solution; (2) it takes a long time to learn and train [7, 8]. Although previous studies have made relevant attempts to optimize the BP algorithm, however, no comparisons have been made among the optimization algorithms, and the emergence of some new optimization algorithms (e.g., Levenberg a Marquardt) has further enriched the library of optimization algorithms and requires comparative studies [9, 10].Based on this, this paper investigates the use of artificial neural networks with optimized BP algorithms to predict socioeconomic development, analyze socioeconomic development trends and change patterns, and conduct a comparative study between optimized BP algorithms using GDP data in 2021. The results show that the prediction model constructed in this paper has a good prediction effect, and the research results can provide a theoretical basis for the scientific formulation of macroeconomic control policies.


## 3. Data Sources and Preprocessing

### 3.1. Selection of Variables

It has been pointed out in the previous analysis that GDP is one of the most important and commonly used measures to evaluate socioeconomic development [11–13]. In this study, data related to the economy and exports of Jiangsu Province from 2001 to 2020 are used to apply monetary policy tools to evaluate government services in economic activities, adjusting to the current status quo, and maintaining a balanced relationship between economic growth and fixed asset investment. In this study, China’s GDP is used as the explanatory variable (Y). In the selection of explanatory variables, total foreign trade exports are taken as the core explanatory variable (X1); the existence of capital accumulation effect is of great benefit to the exports of local enterprises, so the total investment of foreign-invested enterprises is selected as the second explanatory variable (X2); considering the expenditure method of GNP Y=C + I + G, the total retail sales of social consumer goods and government expenditure are selected as the third. Since the trend of exchange rate of RMB against USD is more related to the trend of economic growth, the exchange rate of RMB against USD is selected as the last explanatory variable (X5).


### 3.2. Data Collection

In the selection of the data time period, this paper takes China’s accession to the WTO in 2001 as the starting point of the sample period, limited by data availability, and chooses 2020 as the end point of the sample period, total consumption, government expenditure, and other data were collected, the research data was processed by SPSS22.0 software, and the software used for graphing was GraphPadPrism8. The macroeconomic data used in this article are from the National Bureau of Statistics, China Statistical Yearbook, and Nanjing Customs, and the data on the exchange rate of RMB against the US dollar are from the Sina Finance website. The specific data are shown in Table1.Table 1
Economic and export-related data of Jiangsu Province, 2001–2020.
YearGDP (billion yuan)Total exports (US$ million)Foreign direct investment (US$ million)Total consumption (billion yuan)Government spending (billion yuan)Exchange rates20019456.82887.492001.02869.01114.38.3200210606.93846.5125484.03215.81326.88.3200312442.95911.3150014.03566.51574.58.3200414823.18749.4216981.04159.71798.28.3200518121.312296.7265720.05735.52319.68.1200621240.816041.0324300.06706.22809.57.8200725988.420361.0382030.07985.93605.57.4200830945.523802.9415930.09905.14417.86.9200934471.719919.9444400.011484.15140.06.8201041383.927053.9508106.013606.36295.36.6201148839.231259.0572851.016058.37115.16.6201253701.932852.4625000.018411.17329.06.3201359349.432880.2666376.020878.27720.66.1201464830.534183.3718131.023458.18556.86.1201571255.933864.5782154.026710.19796.36.5201677350.931905.3879868.029612.510845.26.9201785869.836302.6965819.032818.211128.46.6201893207.640397.51056042.035472.612023.06.9201998656.839482.81173515.037672.512573.36.92020102807.739612.71369729.037086.113681.66.6In practical applications, to improve the training sensitivity and speed as well as to avoid the saturation region of the Sigmoid function, if the collected data span a large area, the input data are generally required to have values between 0 and 1. Therefore, it is necessary to perform preprocessing operations on the input data. It is usually required to perform preprocessing separately for different variables or to perform uniform preprocessing operations for variables with similar properties. If the output layer node also uses the Sigmoid transformation function, it is also necessary to perform the corresponding preprocessing operations on the output variables. There are many methods of preprocessing. However, it is important to note that after the training of the preprocessed data, it is necessary to perform an inverse transformation on the output of the network to obtain the actual values. In addition, to ensure that the constructed model has a certain degree of extrapolation capability, it is best to keep the preprocessed data values in the range of 0.2–0.8, leaving a space between 0–0.2 and 0.8–1.0. The following are the data preprocessing and inverse preprocessing algorithms.Data preprocessing algorithms:(1)y=0.8−0.2∗x−minxmaxx−minx+0.2.Antipreprocessing algorithm:(2)x=y−0.2∗maxx−minx0.8−0.2+minx,where the original GDP data is represented by x; the preprocessed GDP data is represented by y; the minimum and maximum values of the range of x values are represented by min(x) and max(x), respectively, and the min-max method is applied to ensure the accuracy of the data.


### 3.3. Modeling

The predictor variable is defined as GDP, and the input variables are defined as fixed asset investment, money supply, and fiscal revenue. Based on the modeling between each variable, GDP =f (FAI + MS + FR), FAI, MS, and FR denote fixed asset investment, money supply, and fiscal revenue, respectively, and f is the expression of FAI, MS, and FR on GDP. The 2001–2020 data are used as the test set, and the lagged one-period algorithm is chosen to forecast the GDP values because the prior indicator pairs can predict the future GDP values.


### 3.4. Deep Learning Prediction

Output the GDP value of Jiangsu Province from 2001 to 2020 and compare the GDP forecast value with the real value, see Table2.Table 2
Economic and export-related data of Jiangsu Province, 2001–2020.
YearGDP real value (billion yuan)GDP forecast (billion yuan)Absolute errorRelative error (%)20019456.88403.8105311.1200210606.99352.9125411.8200312442.910700.9174214.0200414823.113395.114289.6200518121.316436.316859.3200621240.819813.814276.7200725988.424378.416106.2200830945.528518.524277.8200934471.733539.79322.7201041383.940658.97251.8201148839.245424.234157.0201253701.952219.914822.8201359349.458402.49472.4201464830.564098.57321.1201571255.968681.925743.6201677350.97574016102.1201785869.884111.817582.0201893207.69177814291.5201998656.897230.814261.42020102807.7101572.712351.2Absolute error of GDP forecast value = absolute error of GDP forecast value-relative error value; relative error value = absolute error/absolute error of GDP forecast value.


### 3.5. Analysis of the Difference between the Real Value and the Predicted Value of GDP from 2001 to 2020

The results in Table2 show that the average relative error of the forecast values is 5.3%, which fluctuates up and down around the true value, but the range of fluctuation is not large. The previous year’s indicators enable the prediction of the next year’s GDP values. To give a more visual representation of the difference between the real and forecasted GDP values, Figure 4 shows this.Figure 4
Analysis of the difference between the actual GDP value and the forecast value.The results of this study were compared with those of other scholars to explore the difference between traditional prediction methods and deep neural network-based prediction methods, and the model prediction results were analyzed by applying the mean absolute percentage error and root mean square error, and the relative index and absolute index were positively related to the model prediction accuracy, and the larger the value of the relative index and absolute index, the larger the error and the poorer the prediction effect based on the deep neural network.


### 3.6. Analysis of Prediction Accuracy

The results in Table3 show that compared with the prediction results of ARMA, logistic method, exponential regression method, and moving average method, the deep neural network prediction method has smaller prediction error and higher prediction accuracy and precision, which makes the effectiveness of prediction based on the deep neural network significantly improved. There have been studies on the free combination of various models to form a new prediction model after the analysis of the prediction value, which can provide a good basis for the assessment of social development forms [14–17]. Nevertheless, the forecasting approach also has some limitations, all the forecasting models are linear forecasting, which is an important way to forecast GDP, and although it has higher accuracy compared with the traditional forecasting approach, the validity has to continue to be improved [18]. This study was carried out on the basis of others’ studies, choosing nonlinear structural models and relevant indicators for forecasting, counting GDP values, and comparing different forecasting results to confirm the validity and feasibility of the forecasting results [19, 20].Table 3
The MAPE, RMSE, and average prediction error analysis of different prediction methods.
Rating indicatorsARMALogistic methodExponential regression methodDeep neural networkprediction methodMoving averageMAPE7.3%18.5%14.8%3.9%6.8%Average prediction error21.4%31.5%21.7%21.2%31.6%RMSE46561.351264.248956.719234.27.1242682.4


## 3.1. Selection of Variables

It has been pointed out in the previous analysis that GDP is one of the most important and commonly used measures to evaluate socioeconomic development [11–13]. In this study, data related to the economy and exports of Jiangsu Province from 2001 to 2020 are used to apply monetary policy tools to evaluate government services in economic activities, adjusting to the current status quo, and maintaining a balanced relationship between economic growth and fixed asset investment. In this study, China’s GDP is used as the explanatory variable (Y). In the selection of explanatory variables, total foreign trade exports are taken as the core explanatory variable (X1); the existence of capital accumulation effect is of great benefit to the exports of local enterprises, so the total investment of foreign-invested enterprises is selected as the second explanatory variable (X2); considering the expenditure method of GNP Y=C + I + G, the total retail sales of social consumer goods and government expenditure are selected as the third. Since the trend of exchange rate of RMB against USD is more related to the trend of economic growth, the exchange rate of RMB against USD is selected as the last explanatory variable (X5).


## 3.2. Data Collection

In the selection of the data time period, this paper takes China’s accession to the WTO in 2001 as the starting point of the sample period, limited by data availability, and chooses 2020 as the end point of the sample period, total consumption, government expenditure, and other data were collected, the research data was processed by SPSS22.0 software, and the software used for graphing was GraphPadPrism8. The macroeconomic data used in this article are from the National Bureau of Statistics, China Statistical Yearbook, and Nanjing Customs, and the data on the exchange rate of RMB against the US dollar are from the Sina Finance website. The specific data are shown in Table1.Table 1
Economic and export-related data of Jiangsu Province, 2001–2020.
YearGDP (billion yuan)Total exports (US$ million)Foreign direct investment (US$ million)Total consumption (billion yuan)Government spending (billion yuan)Exchange rates20019456.82887.492001.02869.01114.38.3200210606.93846.5125484.03215.81326.88.3200312442.95911.3150014.03566.51574.58.3200414823.18749.4216981.04159.71798.28.3200518121.312296.7265720.05735.52319.68.1200621240.816041.0324300.06706.22809.57.8200725988.420361.0382030.07985.93605.57.4200830945.523802.9415930.09905.14417.86.9200934471.719919.9444400.011484.15140.06.8201041383.927053.9508106.013606.36295.36.6201148839.231259.0572851.016058.37115.16.6201253701.932852.4625000.018411.17329.06.3201359349.432880.2666376.020878.27720.66.1201464830.534183.3718131.023458.18556.86.1201571255.933864.5782154.026710.19796.36.5201677350.931905.3879868.029612.510845.26.9201785869.836302.6965819.032818.211128.46.6201893207.640397.51056042.035472.612023.06.9201998656.839482.81173515.037672.512573.36.92020102807.739612.71369729.037086.113681.66.6In practical applications, to improve the training sensitivity and speed as well as to avoid the saturation region of the Sigmoid function, if the collected data span a large area, the input data are generally required to have values between 0 and 1. Therefore, it is necessary to perform preprocessing operations on the input data. It is usually required to perform preprocessing separately for different variables or to perform uniform preprocessing operations for variables with similar properties. If the output layer node also uses the Sigmoid transformation function, it is also necessary to perform the corresponding preprocessing operations on the output variables. There are many methods of preprocessing. However, it is important to note that after the training of the preprocessed data, it is necessary to perform an inverse transformation on the output of the network to obtain the actual values. In addition, to ensure that the constructed model has a certain degree of extrapolation capability, it is best to keep the preprocessed data values in the range of 0.2–0.8, leaving a space between 0–0.2 and 0.8–1.0. The following are the data preprocessing and inverse preprocessing algorithms.Data preprocessing algorithms:(1)y=0.8−0.2∗x−minxmaxx−minx+0.2.Antipreprocessing algorithm:(2)x=y−0.2∗maxx−minx0.8−0.2+minx,where the original GDP data is represented by x; the preprocessed GDP data is represented by y; the minimum and maximum values of the range of x values are represented by min(x) and max(x), respectively, and the min-max method is applied to ensure the accuracy of the data.


## 3.3. Modeling

The predictor variable is defined as GDP, and the input variables are defined as fixed asset investment, money supply, and fiscal revenue. Based on the modeling between each variable, GDP =f (FAI + MS + FR), FAI, MS, and FR denote fixed asset investment, money supply, and fiscal revenue, respectively, and f is the expression of FAI, MS, and FR on GDP. The 2001–2020 data are used as the test set, and the lagged one-period algorithm is chosen to forecast the GDP values because the prior indicator pairs can predict the future GDP values.


## 3.4. Deep Learning Prediction

Output the GDP value of Jiangsu Province from 2001 to 2020 and compare the GDP forecast value with the real value, see Table2.Table 2
Economic and export-related data of Jiangsu Province, 2001–2020.
YearGDP real value (billion yuan)GDP forecast (billion yuan)Absolute errorRelative error (%)20019456.88403.8105311.1200210606.99352.9125411.8200312442.910700.9174214.0200414823.113395.114289.6200518121.316436.316859.3200621240.819813.814276.7200725988.424378.416106.2200830945.528518.524277.8200934471.733539.79322.7201041383.940658.97251.8201148839.245424.234157.0201253701.952219.914822.8201359349.458402.49472.4201464830.564098.57321.1201571255.968681.925743.6201677350.97574016102.1201785869.884111.817582.0201893207.69177814291.5201998656.897230.814261.42020102807.7101572.712351.2Absolute error of GDP forecast value = absolute error of GDP forecast value-relative error value; relative error value = absolute error/absolute error of GDP forecast value.


## 3.5. Analysis of the Difference between the Real Value and the Predicted Value of GDP from 2001 to 2020

The results in Table2 show that the average relative error of the forecast values is 5.3%, which fluctuates up and down around the true value, but the range of fluctuation is not large. The previous year’s indicators enable the prediction of the next year’s GDP values. To give a more visual representation of the difference between the real and forecasted GDP values, Figure 4 shows this.Figure 4
Analysis of the difference between the actual GDP value and the forecast value.The results of this study were compared with those of other scholars to explore the difference between traditional prediction methods and deep neural network-based prediction methods, and the model prediction results were analyzed by applying the mean absolute percentage error and root mean square error, and the relative index and absolute index were positively related to the model prediction accuracy, and the larger the value of the relative index and absolute index, the larger the error and the poorer the prediction effect based on the deep neural network.


## 3.6. Analysis of Prediction Accuracy

The results in Table3 show that compared with the prediction results of ARMA, logistic method, exponential regression method, and moving average method, the deep neural network prediction method has smaller prediction error and higher prediction accuracy and precision, which makes the effectiveness of prediction based on the deep neural network significantly improved. There have been studies on the free combination of various models to form a new prediction model after the analysis of the prediction value, which can provide a good basis for the assessment of social development forms [14–17]. Nevertheless, the forecasting approach also has some limitations, all the forecasting models are linear forecasting, which is an important way to forecast GDP, and although it has higher accuracy compared with the traditional forecasting approach, the validity has to continue to be improved [18]. This study was carried out on the basis of others’ studies, choosing nonlinear structural models and relevant indicators for forecasting, counting GDP values, and comparing different forecasting results to confirm the validity and feasibility of the forecasting results [19, 20].Table 3
The MAPE, RMSE, and average prediction error analysis of different prediction methods.
Rating indicatorsARMALogistic methodExponential regression methodDeep neural networkprediction methodMoving averageMAPE7.3%18.5%14.8%3.9%6.8%Average prediction error21.4%31.5%21.7%21.2%31.6%RMSE46561.351264.248956.719234.27.1242682.4


## 4. Conclusion

In this study, we selected the economic and export-related data of Jiangsu Province from 2001 to 2020, built a data model, analyzed the prediction results of GDP values by different forecasting methods, and explored the socioeconomic development trends [21, 22]. The empirical results show that the deep learning neural network-based forecasting approach has higher forecasting value and lower forecasting error values than other traditional forecasting approaches, which can provide a basis and reference for macroeconomic regulation and control of the economy and has good practical significance [23]. In addition, a more in-depth investigation based on the research of other scholars, through the analysis of GDP-related indicators, can effectively combine GDP and prior indicators, which improves the persuasive power of prediction results and has a very important role in socioeconomic forecasting [24]. The application of a deep neural network-based forecasting approach can be adapted to the time-varying characteristics of GDP variable indicators and can further improve the forecasting accuracy. The results of this study are in good agreement with those of other scholars [25]. This study compares the prediction results with the results of other scholars, and the research proposal is highly innovative and feasible, which can provide theoretical support for the next research and facilitate the implementation of economic regulation measures [26]. However, it will also face greater difficulties in the process of effectively predicting the GDP value. Therefore, it needs to continue to be improved in the future. There are deficiencies in time prediction, and the GDP value of the next year can only be predicted through the indicators of the previous year, but the GDP value of the next few years cannot be predicted. In addition, this study only collects data on Jiangsu Province’s GDP, total exports, foreign direct investment, total consumption, and government expenditures from 2001 to 2020, which cannot represent all data. Therefore, more data need to be collected in the future to improve the accuracy of the research data and feasibility. The prediction method based on the deep neural network should adjust the appropriate number of nodes and hidden layers, and compare the weights of the hidden layers in order to improve the prediction accuracy and accuracy of the nonlinear structural model [27, 28].In general, the prediction method based on the deep neural network has achieved good results and can make up for the shortcomings of traditional prediction methods. With the enhancement of computer capabilities, the network prediction mode has been widely used, and is used in medical and health, natural language, processing, and other related fields. Therefore, the prediction model based on the deep neural network should be further promoted and applied.



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