# Convolutional Neural Network-Based Algorithm for Predicting the Gross Marine Product in Fujian Province

Authors: Tao Liu; Juan Shan
Journal: Security and Communication Networks
 (2022)

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## Abstract

Ocean economy is the sum of various industrial activities for developing, utilizing, and protecting the ocean, as well as the activities associated with it. As the globe gradually pays more attention to ocean economic growth, China, as a significant maritime country, has begun to pay attention to the sector and put up ocean economic development policies. The marine economy has great potential, and the development of the marine economy is of great significance to the economic development of China. The Fujian Province is located at the junction of the Belt and Road and the Yangtze River Economic Belt, with a unique geographical location and natural resource advantages. Analyzing the state of the ocean economy in the Fujian Province, identifying challenges, and developing ocean economic development strategies to promote the long-term growth of the ocean economy in the Fujian Province has become a pressing issue that must be addressed. Because there is currently limited research on ocean economic systems and a paucity of ocean economic data, this paper uses a deep neural network to forecast the ocean production output value of the Fujian Province. The experimental results show that the model proposed in this paper can predict the ocean invention value of the Fujian Province well, which confirms the effectiveness of the model.


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## Body

## 1. Introduction

With the gradual depletion of land resources, the discovery of the value of ocean resources, and the progressive development of science and technology, the importance of the development and utilization of the ocean has become a worldwide consensus. After the Second World War, various coastal countries have seen the ocean as an important pole for resource development and wealth growth [1]. The U.S. government has focused on the protection of ocean resources since the mid-twentieth century. In September 1945, President Harry S. Truman issued the Continental Shelf Proclamation, asserting U.S. jurisdiction and control over the ocean resources adjacent to its shores. In 1961, President John F. Kennedy told Congress that “in order to survive, the United States must use the oceans as a place of exploration [2]. In 1970, the United States established the National Oceanic and Atmospheric Administration, whose functions include the management of the use and protection of ocean and coastal resources. Represented by the United States and France, developed countries have marched to the ocean, vigorously developed the ocean economy, and created unprecedented achievements, bringing about a major worldwide trend of developing the ocean [3].Since 2000, China has begun to pay attention to the value of the ocean to economic development, and the importance of ocean economic development has been implemented to the government’s decision-making and operational level, ushering in the best overall development period in the history of China’s ocean industry [4]. In 2003, the State Council released for the first time the “Outline of the National Ocean Economic Development Plan”, in which it clarified the importance of the development of the ocean economy to adjust the economic layout of coastal areas, industrial structure and promote the rapid development of the national economy [5]. In 2012, the report of the 18th National Congress proposed “building a strong country in the ocean”, and since the 18th National Congress, “a strong country in the ocean” has become a major strategic deployment in China, and the ocean economy has been mentioned in an unprecedentedly prominent position [6]. In May 2013, the State Oceanic Administration released China’s first ocean economic report; in 2013 and 2017, the State Council released the 12th and 13th five-year plans for ocean economic development respectively. In November 2017, the report of the 19th Party Congress explicitly called for “adhering to the integration of land and sea and accelerating the construction of a strong ocean state” [7]. The development of the ocean economy has never been so close to national development and people’s lives as it is today.The Fujian Province, as the economic hinterland of the Yangtze River, facing the vast ocean provinces, ushered in a variety of development prospects in China’s attempts to develop the sea, the ocean economy, and the environment [8]. In March 2011, the national 12th Five-Year Plan outline clearly puts forward the focus on promoting the development of the Fujian Province, becoming the first national strategic regional planning with the theme of the ocean economy [9]. At this stage, the research literature on the ocean economy is gradually increasing, but most of the literature on the study of a region’s ocean economy only uses qualitative analysis, and the quantitative analysis used in this paper is also an enrichment to the research in this field [10]. The framework diagram of the gross ocean product is shown in Figure 1.Figure 1
Framework diagram of the gross ocean product.This paper attempts to propose a more comprehensive quantitative analysis for the study of a region’s ocean economy: a longitudinal study of the efficiency of a region’s ocean economy by means of data envelopment analysis, and a horizontal comparison analysis of its ocean economy by means of exploratory factor analysis, both of which are combined to arrive at an objective judgment of the region’s ocean economy [11]. The practical significance of this research is to study the strategies adapted to the development of the ocean economy in the Fujian Province based on current information and data with timeliness, which can provide policy suggestions for the development of the ocean economy in the Fujian Province in the context of FTA in practical application [12]. Therefore, the development strategy of the Fujian Province’s ocean economy has the significance of demonstrating and driving the whole country [13]. In view of the currently limited research on the ocean economic systems and the lack of ocean economic data, this paper makes predictions on the ocean production output value of the Fujian Province by building a deep neural network [14]. The experimental results show that the model proposed in this paper can predict the ocean production value of the Fujian Province well, which verifies the effectiveness of the model. The research in this paper lays the foundation for the national ocean economic forecast.The organization paragraph is as follows: Section2 overviews the related work. Section 3 discusses the design of the model. Section 4 discusses the experiments and results. Section 5 concludes the article.


## 2. Related Work

This chapter provides an overview of related work, starting with the current state of research on ocean economics, followed by the content of ocean economics, and finally the current state of research on ocean economic prediction algorithms.


### 2.1. Current Status of Research in Ocean Economy Research

The study of the ocean economy began with the study of ocean industries, and this phase also began to establish the link between the ocean and the economy, i.e., to define the ocean economy [15]. Scientists studied the impact of 13 ocean-related companies on the regional economy in New England in 1967 and published a report on the role of ocean industries in economic development. In the following phase, the research topics centered on how to quantify and measure the ocean economy [16]. Science published an article on the analysis of 66 ocean-related industries screened in the national income accounts through the theory of national income accounts to estimate the contribution of the U.S. oceans to the national economy. The ocean economy is separated into ocean and coastal components in the United States, and scientists have examined the coastal zone economy, which is acknowledged as a major driver of the national economy [17]. In general, studies of the ocean economy began in the late 1990s, and UNESCO designated 1998 as the “Year of the Ocean.” Studies on the accounting and assessment of the ocean economy have been developed in Asian countries, including Japan, Korea, the Philippines, Indonesia, and Thailand. Compared with the ocean economy for macroscopic research, foreign scholars concentrate more on the development of certain ocean industries, combining the methods of social investigation and applying scientific models such as ecological economics to the research process [18]. In coastal tourism, for example, the model of ocean tourism planning and administration with community or nongovernmental organization participation is a widely known strategy. By developing a bio economics model, we discuss the environmental impact of the exploitation of commercial ocean fishery resources and provide its reference for the formulation of fishery policies.The research activities of domestic scholars on the ocean economy are divided into three stages: First, in the early 1980s, the research focused on disciplinary tasks; in June 1981, the State Oceanic Administration held the first symposium on ocean economy research, which eventually resulted in a collection of papers [19]. The second was the stage in the mid to late 1980s when regional research was the focus. At that time, scholars and the government conducted a lot of research on coastal zone economy, island economy, and ocean resource economy [20]. The third is the period since the 1990s when local ocean development planning has been the subject of study. The study of the features of the ocean economy and its contribution to the economy is a major focus of research on the fundamental issues of the ocean economy. Traditional ocean industries, developing ocean industries, and future ocean industries were categorized into three groups by the researcher, who stressed their importance to economic development [21].The accounting structure of ocean GDP is shown in Figure 2.Figure 2
Accounting structure of ocean GDP.The researcher then analyzed the correlation degree of major ocean industries in China and coastal provinces and cities, predicted the output value of China’s ocean economy, and concluded that China’s ocean industries have unreasonable institutions, the low contribution rate of science and technology, and high impact of ocean disasters [22]. The researcher looked at the pull effect of the three industries in a comprehensive analysis of China’s ocean economic development and concluded that the ocean economy’s ability to absorb employment is much higher than the national economy’s in the same period, and the ocean economy will usher in rapid growth.


### 2.2. Research Content of the Ocean Economy

The term “ocean economy” was developed with the development of ocean economic activities, and its connotation and research content have been evolving since its emergence [23]. In general, despite the publication of several monographs and textbooks on the ocean economy, the views on the ocean economy have not been fully unified in academic circles; therefore, several views will be cited in the following discussion of ocean economy theory. Foreign scholars are less likely to use the term “ocean economy” for research, mainly because at present, foreign scholars are more concerned with the research on the growth of specific ocean industry sectors or regional ocean economic development laws, focusing on microscopic phenomenon description, and less research is conducted on the overall general laws of the ocean economy from a macroscopic perspective [24].Since 1978, economists in China have proposed the creation of “ocean economics” as a new discipline, scholars have devoted significant attention to the study of the ocean economy as a whole and the general development law, and many scholars have attempted to establish the theoretical system of ocean economics as an independent discipline, presenting its research tasks and research values [25]. In addition to the above, the State Oceanic Administration (SOA) has published a statistical bulletin on China’s ocean economy since 2003, which defines the ocean economy as “the sum of various industries and related economic activities that exploit the ocean”. Because the definition of the ocean economy is not uniform, and the ocean economy itself is a complex system, there is a rich discussion of its research content [26]. The research on the ocean economy should be based on the application of the basic principles of modern economics to the practice of ocean economic activities.


### 2.3. Research Status of Ocean Economic Prediction Algorithms

A variety of methods have been applied to ocean economic forecasting, and the commonly used methods can be broadly classified into qualitative forecasting methods, quantitative forecasting methods, and data mining-based forecasting methods. The following is a brief introduction to these three forecasting methods and focuses on the current status of research on data mining-based ocean economic forecasting [27].The qualitative forecasting method is a more intuitive and subjective ocean economic forecasting method, mainly by the relevant experts based on the past and present information of the forecast object as well as personal experience and judgment ability, to make advance judgment and speculation on the future development trend and the law of financial activities [28]. The prediction findings can be severely biased when the expert’s knowledge, competence, and experience are insufficient. The quantitative forecasting approach is a statistic-based forecasting strategy. It is built on realistic issue assumptions and employs probabilistic statistical methods on a theoretical derivation to produce a final forecasting model, which is then used to construct forecasts by estimating the structural parameters of the model [29]. This prediction method requires a high level of the mathematical theoretical foundation for the modeler, as well as in-depth knowledge of the system. Data mining, also known as knowledge discovery, is the process of discovering interesting and implicitly exploitable information from large-scale data, as well as potential, inherent, and useful patterns or models in the data itself. A class of methods commonly used in the field of ocean economic forecasting is a single neural network or a combination of neural networks with other methods [30]. The use of neural networks for financial forecasting is effective in detecting nonlinear, nonadditive, and other links in the data, and can handle continuous and categorical predictor variables to obtain good forecasting results. An ocean economic time series is a set of series in which various historical data generated from economic activities are arranged in the order of their occurrence.


## 2.1. Current Status of Research in Ocean Economy Research

The study of the ocean economy began with the study of ocean industries, and this phase also began to establish the link between the ocean and the economy, i.e., to define the ocean economy [15]. Scientists studied the impact of 13 ocean-related companies on the regional economy in New England in 1967 and published a report on the role of ocean industries in economic development. In the following phase, the research topics centered on how to quantify and measure the ocean economy [16]. Science published an article on the analysis of 66 ocean-related industries screened in the national income accounts through the theory of national income accounts to estimate the contribution of the U.S. oceans to the national economy. The ocean economy is separated into ocean and coastal components in the United States, and scientists have examined the coastal zone economy, which is acknowledged as a major driver of the national economy [17]. In general, studies of the ocean economy began in the late 1990s, and UNESCO designated 1998 as the “Year of the Ocean.” Studies on the accounting and assessment of the ocean economy have been developed in Asian countries, including Japan, Korea, the Philippines, Indonesia, and Thailand. Compared with the ocean economy for macroscopic research, foreign scholars concentrate more on the development of certain ocean industries, combining the methods of social investigation and applying scientific models such as ecological economics to the research process [18]. In coastal tourism, for example, the model of ocean tourism planning and administration with community or nongovernmental organization participation is a widely known strategy. By developing a bio economics model, we discuss the environmental impact of the exploitation of commercial ocean fishery resources and provide its reference for the formulation of fishery policies.The research activities of domestic scholars on the ocean economy are divided into three stages: First, in the early 1980s, the research focused on disciplinary tasks; in June 1981, the State Oceanic Administration held the first symposium on ocean economy research, which eventually resulted in a collection of papers [19]. The second was the stage in the mid to late 1980s when regional research was the focus. At that time, scholars and the government conducted a lot of research on coastal zone economy, island economy, and ocean resource economy [20]. The third is the period since the 1990s when local ocean development planning has been the subject of study. The study of the features of the ocean economy and its contribution to the economy is a major focus of research on the fundamental issues of the ocean economy. Traditional ocean industries, developing ocean industries, and future ocean industries were categorized into three groups by the researcher, who stressed their importance to economic development [21].The accounting structure of ocean GDP is shown in Figure 2.Figure 2
Accounting structure of ocean GDP.The researcher then analyzed the correlation degree of major ocean industries in China and coastal provinces and cities, predicted the output value of China’s ocean economy, and concluded that China’s ocean industries have unreasonable institutions, the low contribution rate of science and technology, and high impact of ocean disasters [22]. The researcher looked at the pull effect of the three industries in a comprehensive analysis of China’s ocean economic development and concluded that the ocean economy’s ability to absorb employment is much higher than the national economy’s in the same period, and the ocean economy will usher in rapid growth.


## 2.2. Research Content of the Ocean Economy

The term “ocean economy” was developed with the development of ocean economic activities, and its connotation and research content have been evolving since its emergence [23]. In general, despite the publication of several monographs and textbooks on the ocean economy, the views on the ocean economy have not been fully unified in academic circles; therefore, several views will be cited in the following discussion of ocean economy theory. Foreign scholars are less likely to use the term “ocean economy” for research, mainly because at present, foreign scholars are more concerned with the research on the growth of specific ocean industry sectors or regional ocean economic development laws, focusing on microscopic phenomenon description, and less research is conducted on the overall general laws of the ocean economy from a macroscopic perspective [24].Since 1978, economists in China have proposed the creation of “ocean economics” as a new discipline, scholars have devoted significant attention to the study of the ocean economy as a whole and the general development law, and many scholars have attempted to establish the theoretical system of ocean economics as an independent discipline, presenting its research tasks and research values [25]. In addition to the above, the State Oceanic Administration (SOA) has published a statistical bulletin on China’s ocean economy since 2003, which defines the ocean economy as “the sum of various industries and related economic activities that exploit the ocean”. Because the definition of the ocean economy is not uniform, and the ocean economy itself is a complex system, there is a rich discussion of its research content [26]. The research on the ocean economy should be based on the application of the basic principles of modern economics to the practice of ocean economic activities.


## 2.3. Research Status of Ocean Economic Prediction Algorithms

A variety of methods have been applied to ocean economic forecasting, and the commonly used methods can be broadly classified into qualitative forecasting methods, quantitative forecasting methods, and data mining-based forecasting methods. The following is a brief introduction to these three forecasting methods and focuses on the current status of research on data mining-based ocean economic forecasting [27].The qualitative forecasting method is a more intuitive and subjective ocean economic forecasting method, mainly by the relevant experts based on the past and present information of the forecast object as well as personal experience and judgment ability, to make advance judgment and speculation on the future development trend and the law of financial activities [28]. The prediction findings can be severely biased when the expert’s knowledge, competence, and experience are insufficient. The quantitative forecasting approach is a statistic-based forecasting strategy. It is built on realistic issue assumptions and employs probabilistic statistical methods on a theoretical derivation to produce a final forecasting model, which is then used to construct forecasts by estimating the structural parameters of the model [29]. This prediction method requires a high level of the mathematical theoretical foundation for the modeler, as well as in-depth knowledge of the system. Data mining, also known as knowledge discovery, is the process of discovering interesting and implicitly exploitable information from large-scale data, as well as potential, inherent, and useful patterns or models in the data itself. A class of methods commonly used in the field of ocean economic forecasting is a single neural network or a combination of neural networks with other methods [30]. The use of neural networks for financial forecasting is effective in detecting nonlinear, nonadditive, and other links in the data, and can handle continuous and categorical predictor variables to obtain good forecasting results. An ocean economic time series is a set of series in which various historical data generated from economic activities are arranged in the order of their occurrence.


## 3. Design of the Model

This chapter defines the basic concept and principle of convolutional neural network and construction of prediction model of ocean GDP in detail.


### 3.1. Basic Concept and Principle of the Convolutional Neural Network

In the field of computer science and artificial intelligence, artificial neural networks mimic the information propagation mechanisms of the biological nervous system, enabling machines to mimic the human brain for learning and recognizing information. Neural networks compute and propagate information through a large number of interconnected neurons and are often used to describe complex relationships between inputs and outputs or to explore the intrinsic structure and patterns of data. Artificial neurons are simulations of the characteristics of weighted summation of input information of biological neurons, which use activation functions to map the obtained network inputs to achieve amplification or suppression processing of neuron output values. A single neuron can only achieve very simple functions. To achieve functional simulation of human brain neural networks and to accomplish complex tasks, a large number of neurons need to be connected in a certain way to form artificial neural networks with different topologies.Deep learning is a new research direction in artificial intelligence. Deep learning networks are derived from the imitation of the structure of the human cerebral cortex. The human cerebral cortex understands the received information in a hierarchical manner. Similarly, deep learning is usually hierarchical in structure, layering the input data to the network. The purpose of deep learning methods is to find the true relationships within the original data. Typical deep learning network structures are deep confidence networks, stacked self-coding networks, and convolutional neural networks. The deep confidence network is a directed connected network model with the structure diagram shown in Figure3.Figure 3
Structure diagram of the deep confidence network.A deep confidence network containing two hidden layers can be mathematically represented by the following joint probability distribution.(1)pv,h1,h2,θ=pv|h1;W1ph1,h2;W2.It can be seen that the training process of a deep confidence network is the process of estimating the great likelihood of the input data. A Boltzmann machine is an undirected graph probability model based on energy theory, so the energy function of the restricted Boltzmann machine model is defined by the following mathematical expression.(2)Ev,h;θ=−12vTWh−vTb−hTc.Then, the mathematical expression of the probability of the network model for a given input datav is as follows.(3)pv;θ=p∗v;θZθ=1Zθ∑hexp−Ev,h;θ.The structure of a stacked self-coding network is similar to that of a deep confidence network in that it consists of several basic structural units connected to each other. However, the basic structural units of the stacked self-coding network are self-coding networks. A self-coding network is a two-layer neural network consisting of an encoding layer and a decoding layer. Since the activation functions of the implicit layers are all non-linear, the self-coding networks are able to describe multiple forms of distribution in the input data, which makes stacked self-coding networks consisting of multiple self-coding networks connected sequentially have the ability to describe complex features such as higher-order nonlinearities in the data. During the pretraining process, a certain amount of noise is added in the connection weight finetuning phase in order to avoid overfitting of the stacked self-coding network to the training samples. The basic structure of the stacked self-coding network is shown in Figure4.Figure 4
Basic structure of the stacked self-coding network.Shrinkage self-coding networks impose penalty terms on the connection weights of the network by using the Jacobi matrices in order to keep the model somewhat invariant to perturbations of the input data. Preventing self-coding network models from learning constant mapping relationships that do not have coding functions and overcoming the limitation that general self-coding networks require the number of samples to be larger than the number of sample dimensions by minimizing the de-manipulated reconstruction error and reconstructing the true original input from the original input data containing random noise. Although many works have been carried out by domestic and foreign scholars on improving convolutional neural networks, there are still many problems that need further solutions. Currently, one of the main problems of convolutional neural networks is that their performance is limited by the amount of data required by the network, which makes it difficult for convolutional neural networks to perform recognition tasks with small data sizes. Another problem is that scholars still lack knowledge of the basic principles of convolutional neural networks, and the initialization of parameters and the selection of hyperparameters rely mainly on experience or continuous debugging with the help of a large number of numerical simulation experiments. There are also many optimization ideas for the convolutional neural network, such as the optimization of activation function, the optimization based on network interpretability, and so on. This paper believes that with the gradual improvement of the mathematical description of the convolutional neural network, more optimization methods based on mathematical principles will appear.


### 3.2. Construction of the Prediction Model of Ocean GDP

Convolution and variable pull of a single channel’s input matrix yields the multichannel characteristic layer. Different channels have varying degrees of importance in the convolution process. The characteristic layer of each channel and the convolution kernel are multiplied and added element by element in the classic convolution operation, and the differences of each channel in the training process are ignored. Therefore, based on the traditional convolution, this paper introduces the convolution layer based on an attention mechanism to optimize the effect of feature reduction. Compared with the ordinary convolution layer, the convolution layer based on the attention mechanism introduces an attention calculation link based on a soft attention mechanism between the input characteristic matrix and the convolution kernel convolution operation. The specific procedure is as follows: first, each feature channel’s global mean is pooled in the spatial dimension. A two-dimensional characteristic matrix of each channel is transformed into a real number with a global receptive field. The input method of the ocean GDP data set is shown in Figure5.Figure 5
Input method of the ocean GDP data set.The weight distribution of channel questions is carried out according to the attention value. The input feature matrix and attention value are multiplied element by element to distinguish the importance of different channels, which can better optimize the extracted effective information and the effect of feature reduction. Table1 gives the range of values of the convolution kernel size corresponding to different input sample lengths for two layers of convolution.Table 1
Relationship between the input sample length and convolution kernel size.
Sample lengthCK1CK2CK3CK430733140139525015117360171395Clipping mean pooling picks the mean in the pooling window with the minimum value removed as the final output feature. This strategy avoids network instability caused by too much local information loss, as well as the problem of a region’s activation value being drastically reduced. Because different pooling methods have their own advantages and disadvantages, in order to integrate the advantages of different pooling methods and make up for their shortcomings, this paper takes the output of the convolution layer based on the attention mechanism as the input of the pooling layer, and carries out the maximum pooling, mean pooling, random pooling and cut mean pooling operations respectively. The formula for filtering noise is as follows.(4)mhx=∑t=1TKhx−XtYt∑t=1TKhx−Xt,Khx=1h2πe−x2/2h2.Each neuron in the full connection layer is fully connected with all neurons in the previous layer to provide input for the attention layer. The full connection layer can integrate the local information in the attention layer and map the features learned by the attention layer to the sample tag space. The overall flowchart of the model is shown in Figure6.Figure 6
Overall flowchart of the model.This chapter studies feature reduction and its application in the marine economy. Aiming at the problem of feature loss caused by ignoring the importance of features in feature extraction, CNN introduces a new idea of attention mechanism to improve convolution and pooling structure, optimizes the effect of feature reduction, and gives a marine economy prediction method based on the improved convolution neural network.


## 3.1. Basic Concept and Principle of the Convolutional Neural Network

In the field of computer science and artificial intelligence, artificial neural networks mimic the information propagation mechanisms of the biological nervous system, enabling machines to mimic the human brain for learning and recognizing information. Neural networks compute and propagate information through a large number of interconnected neurons and are often used to describe complex relationships between inputs and outputs or to explore the intrinsic structure and patterns of data. Artificial neurons are simulations of the characteristics of weighted summation of input information of biological neurons, which use activation functions to map the obtained network inputs to achieve amplification or suppression processing of neuron output values. A single neuron can only achieve very simple functions. To achieve functional simulation of human brain neural networks and to accomplish complex tasks, a large number of neurons need to be connected in a certain way to form artificial neural networks with different topologies.Deep learning is a new research direction in artificial intelligence. Deep learning networks are derived from the imitation of the structure of the human cerebral cortex. The human cerebral cortex understands the received information in a hierarchical manner. Similarly, deep learning is usually hierarchical in structure, layering the input data to the network. The purpose of deep learning methods is to find the true relationships within the original data. Typical deep learning network structures are deep confidence networks, stacked self-coding networks, and convolutional neural networks. The deep confidence network is a directed connected network model with the structure diagram shown in Figure3.Figure 3
Structure diagram of the deep confidence network.A deep confidence network containing two hidden layers can be mathematically represented by the following joint probability distribution.(1)pv,h1,h2,θ=pv|h1;W1ph1,h2;W2.It can be seen that the training process of a deep confidence network is the process of estimating the great likelihood of the input data. A Boltzmann machine is an undirected graph probability model based on energy theory, so the energy function of the restricted Boltzmann machine model is defined by the following mathematical expression.(2)Ev,h;θ=−12vTWh−vTb−hTc.Then, the mathematical expression of the probability of the network model for a given input datav is as follows.(3)pv;θ=p∗v;θZθ=1Zθ∑hexp−Ev,h;θ.The structure of a stacked self-coding network is similar to that of a deep confidence network in that it consists of several basic structural units connected to each other. However, the basic structural units of the stacked self-coding network are self-coding networks. A self-coding network is a two-layer neural network consisting of an encoding layer and a decoding layer. Since the activation functions of the implicit layers are all non-linear, the self-coding networks are able to describe multiple forms of distribution in the input data, which makes stacked self-coding networks consisting of multiple self-coding networks connected sequentially have the ability to describe complex features such as higher-order nonlinearities in the data. During the pretraining process, a certain amount of noise is added in the connection weight finetuning phase in order to avoid overfitting of the stacked self-coding network to the training samples. The basic structure of the stacked self-coding network is shown in Figure4.Figure 4
Basic structure of the stacked self-coding network.Shrinkage self-coding networks impose penalty terms on the connection weights of the network by using the Jacobi matrices in order to keep the model somewhat invariant to perturbations of the input data. Preventing self-coding network models from learning constant mapping relationships that do not have coding functions and overcoming the limitation that general self-coding networks require the number of samples to be larger than the number of sample dimensions by minimizing the de-manipulated reconstruction error and reconstructing the true original input from the original input data containing random noise. Although many works have been carried out by domestic and foreign scholars on improving convolutional neural networks, there are still many problems that need further solutions. Currently, one of the main problems of convolutional neural networks is that their performance is limited by the amount of data required by the network, which makes it difficult for convolutional neural networks to perform recognition tasks with small data sizes. Another problem is that scholars still lack knowledge of the basic principles of convolutional neural networks, and the initialization of parameters and the selection of hyperparameters rely mainly on experience or continuous debugging with the help of a large number of numerical simulation experiments. There are also many optimization ideas for the convolutional neural network, such as the optimization of activation function, the optimization based on network interpretability, and so on. This paper believes that with the gradual improvement of the mathematical description of the convolutional neural network, more optimization methods based on mathematical principles will appear.


## 3.2. Construction of the Prediction Model of Ocean GDP

Convolution and variable pull of a single channel’s input matrix yields the multichannel characteristic layer. Different channels have varying degrees of importance in the convolution process. The characteristic layer of each channel and the convolution kernel are multiplied and added element by element in the classic convolution operation, and the differences of each channel in the training process are ignored. Therefore, based on the traditional convolution, this paper introduces the convolution layer based on an attention mechanism to optimize the effect of feature reduction. Compared with the ordinary convolution layer, the convolution layer based on the attention mechanism introduces an attention calculation link based on a soft attention mechanism between the input characteristic matrix and the convolution kernel convolution operation. The specific procedure is as follows: first, each feature channel’s global mean is pooled in the spatial dimension. A two-dimensional characteristic matrix of each channel is transformed into a real number with a global receptive field. The input method of the ocean GDP data set is shown in Figure5.Figure 5
Input method of the ocean GDP data set.The weight distribution of channel questions is carried out according to the attention value. The input feature matrix and attention value are multiplied element by element to distinguish the importance of different channels, which can better optimize the extracted effective information and the effect of feature reduction. Table1 gives the range of values of the convolution kernel size corresponding to different input sample lengths for two layers of convolution.Table 1
Relationship between the input sample length and convolution kernel size.
Sample lengthCK1CK2CK3CK430733140139525015117360171395Clipping mean pooling picks the mean in the pooling window with the minimum value removed as the final output feature. This strategy avoids network instability caused by too much local information loss, as well as the problem of a region’s activation value being drastically reduced. Because different pooling methods have their own advantages and disadvantages, in order to integrate the advantages of different pooling methods and make up for their shortcomings, this paper takes the output of the convolution layer based on the attention mechanism as the input of the pooling layer, and carries out the maximum pooling, mean pooling, random pooling and cut mean pooling operations respectively. The formula for filtering noise is as follows.(4)mhx=∑t=1TKhx−XtYt∑t=1TKhx−Xt,Khx=1h2πe−x2/2h2.Each neuron in the full connection layer is fully connected with all neurons in the previous layer to provide input for the attention layer. The full connection layer can integrate the local information in the attention layer and map the features learned by the attention layer to the sample tag space. The overall flowchart of the model is shown in Figure6.Figure 6
Overall flowchart of the model.This chapter studies feature reduction and its application in the marine economy. Aiming at the problem of feature loss caused by ignoring the importance of features in feature extraction, CNN introduces a new idea of attention mechanism to improve convolution and pooling structure, optimizes the effect of feature reduction, and gives a marine economy prediction method based on the improved convolution neural network.


## 4. Experiments and Results

The experimental design and outcomes are detailed in this chapter. First, it explains the experimental steps and relevant information and data, and then analyzes and discusses the experimental results of the model. The experimental results show that the model can well predict the ocean economic GDP of the Fujian Province and verify the effectiveness of the model.


### 4.1. Experiment Description and Preparation

Since the statistical caliber of China’s ocean economy was revised in 2001, the author chose the annual value added of ocean research, education and management services, ocean-related industries, ocean fisheries, ocean transportation, coastal tourism, ocean shipping industry, and ocean oil and gas industry from 2001 to 2009, which have continuous statistics and are closely related to the ocean GDP, as the input layer of information. Because the BP network’s neurons use an s-type excitation function, the transformation can prevent the neuron output from saturating as a result of the net input’s absolute value being too large, allowing the weights to be adjusted into the flat region of the error surface while taking the network’s training time into account. The normalization formula is as follows.(5)Pk=&maxPk−minPk1.8971−0.1625ln11−Pki'−0.1625+minPk.The learning of a network usually has to have a suitable learning rate. The learning rate determines the amount of change in the weights generated during each training cycle. A large learning rate may lead to unstable network learning. However, a small learning rate can lead to longer training time and slower convergence, so a smaller learning rate is usually preferred to ensure the stability of the network, usually between 0.01 and 0.06. The desired error value is also the best-fit value determined by training the network separately for different error values. The so-called best-fit value is proportional to the number of nodes in the desired implicit layer, and increasing the number of nodes in the implicit layer and the training duration yields a lower error value. The author used the MATLAB neural network toolbox to process the data. The data from 2001 to 2008 were utilized as training samples, while the data from 2009 were used to assess the trained network’s predicting ability. 2001–2009 gross ocean product and the value-added of each ocean industry are shown in Table2.Table 2
Gross ocean product 2001–2009.
YearsGMPMRIMFMTMSIMOGI20019518187732773277417721672002112702090211041104910271020031195223832233423351333123200414622288232424542555235522005176553350475047505710381020062126038053445714565754545200725073443676867686705651262008296625108235883587358535820093196457537893889378936423This paper designs and constructs the evaluation index system for the development level of the ocean economy, and the principles of index selection are completeness, representativeness, orderliness, and operability. Combined with the characteristics of the ocean economy in the Fujian Province, the indicators are selected from the overall development of the ocean economy, the development of each industry, the level of foreign opening, and development potential. The histogram of ocean GDP and added value of various ocean industries is shown in Figure7.Figure 7
Histogram of ocean GDP and added value of various ocean industries.It can be found that the components that have had a dominant influence on the scale of the Zhoushan ocean economy in the past five years are ocean economic scale and development potential factor, ocean ship repair industry influence factor, ocean tertiary industry influence factor, ocean primary industry, ocean economy openness level influence factor, and ocean economy proportion influence factor. The connotation of ocean economic scale and development potential factors includes the size of ocean economic output value, innovation drive, and resource and environment bearing, which are always in the most important influence position, proving the importance of improving ocean research investment and developing new ocean industries.


### 4.2. Experimental Results and Analysis

The influence factor of the proportion of the ocean economy is always in the last place, which indicates that the size of the ocean economy as a proportion of GDP has little influence on the operation of the ocean economy. The impact factor of ocean tertiary industry is in the second and third place, which shows the importance of coastal tourism and port logistics industry in the development of ocean economy today, and the fact that the proportion of ocean tertiary industry in cities with developed ocean economy such as Shanghai is also increasing. In this paper, we did not find the data of all port industries due to the problem of data availability, and the output value of the ship repair industry represents the data of the ocean secondary industry, which will lead to part of the error. The impact factor of ocean secondary industry represented by ship repair industry occupies the second place together with ocean tertiary industry impact factor in 2007 and 2008, which indicates the pulling effect of ocean manufacturing industry on ocean economy. It illustrates the driving effect of developing foreign trade and improving internationalization on the ocean economy in the context of an open economy and Belt and Road. The deep convolutional neural network was trained with eight sets of data from 2001 to 2008 as training samples and tested by simulation. The experimental results are shown in Table3 and Figure 8.Table 3
Simulation results and errors of ocean GDP from 2001 to 2009.
YearsInputOutputError20010.1500.1490.02020020.2580.3490.05020030.2960.4080.15020040.4290.5730.16020050.5470.6850.47020060.6570.7530.65020070.7450.8100.58020080.8210.8420.40020090.8500.8450.540Figure 8
Simulation results and errors of ocean GDP from 2001 to 2009.It can be seen from Table3 and Figure 8 that Fujian keeps one place in the influence factor of the proportion of ocean economy, which is a direct reflection of the high proportion of ocean economy to GDP in the Annex. The current value added of the Zhoushan ocean economy accounts for more than 70% of GDP, but the effect of this factor on the operation of the ocean economy is not obvious.


## 4.1. Experiment Description and Preparation

Since the statistical caliber of China’s ocean economy was revised in 2001, the author chose the annual value added of ocean research, education and management services, ocean-related industries, ocean fisheries, ocean transportation, coastal tourism, ocean shipping industry, and ocean oil and gas industry from 2001 to 2009, which have continuous statistics and are closely related to the ocean GDP, as the input layer of information. Because the BP network’s neurons use an s-type excitation function, the transformation can prevent the neuron output from saturating as a result of the net input’s absolute value being too large, allowing the weights to be adjusted into the flat region of the error surface while taking the network’s training time into account. The normalization formula is as follows.(5)Pk=&maxPk−minPk1.8971−0.1625ln11−Pki'−0.1625+minPk.The learning of a network usually has to have a suitable learning rate. The learning rate determines the amount of change in the weights generated during each training cycle. A large learning rate may lead to unstable network learning. However, a small learning rate can lead to longer training time and slower convergence, so a smaller learning rate is usually preferred to ensure the stability of the network, usually between 0.01 and 0.06. The desired error value is also the best-fit value determined by training the network separately for different error values. The so-called best-fit value is proportional to the number of nodes in the desired implicit layer, and increasing the number of nodes in the implicit layer and the training duration yields a lower error value. The author used the MATLAB neural network toolbox to process the data. The data from 2001 to 2008 were utilized as training samples, while the data from 2009 were used to assess the trained network’s predicting ability. 2001–2009 gross ocean product and the value-added of each ocean industry are shown in Table2.Table 2
Gross ocean product 2001–2009.
YearsGMPMRIMFMTMSIMOGI20019518187732773277417721672002112702090211041104910271020031195223832233423351333123200414622288232424542555235522005176553350475047505710381020062126038053445714565754545200725073443676867686705651262008296625108235883587358535820093196457537893889378936423This paper designs and constructs the evaluation index system for the development level of the ocean economy, and the principles of index selection are completeness, representativeness, orderliness, and operability. Combined with the characteristics of the ocean economy in the Fujian Province, the indicators are selected from the overall development of the ocean economy, the development of each industry, the level of foreign opening, and development potential. The histogram of ocean GDP and added value of various ocean industries is shown in Figure7.Figure 7
Histogram of ocean GDP and added value of various ocean industries.It can be found that the components that have had a dominant influence on the scale of the Zhoushan ocean economy in the past five years are ocean economic scale and development potential factor, ocean ship repair industry influence factor, ocean tertiary industry influence factor, ocean primary industry, ocean economy openness level influence factor, and ocean economy proportion influence factor. The connotation of ocean economic scale and development potential factors includes the size of ocean economic output value, innovation drive, and resource and environment bearing, which are always in the most important influence position, proving the importance of improving ocean research investment and developing new ocean industries.


## 4.2. Experimental Results and Analysis

The influence factor of the proportion of the ocean economy is always in the last place, which indicates that the size of the ocean economy as a proportion of GDP has little influence on the operation of the ocean economy. The impact factor of ocean tertiary industry is in the second and third place, which shows the importance of coastal tourism and port logistics industry in the development of ocean economy today, and the fact that the proportion of ocean tertiary industry in cities with developed ocean economy such as Shanghai is also increasing. In this paper, we did not find the data of all port industries due to the problem of data availability, and the output value of the ship repair industry represents the data of the ocean secondary industry, which will lead to part of the error. The impact factor of ocean secondary industry represented by ship repair industry occupies the second place together with ocean tertiary industry impact factor in 2007 and 2008, which indicates the pulling effect of ocean manufacturing industry on ocean economy. It illustrates the driving effect of developing foreign trade and improving internationalization on the ocean economy in the context of an open economy and Belt and Road. The deep convolutional neural network was trained with eight sets of data from 2001 to 2008 as training samples and tested by simulation. The experimental results are shown in Table3 and Figure 8.Table 3
Simulation results and errors of ocean GDP from 2001 to 2009.
YearsInputOutputError20010.1500.1490.02020020.2580.3490.05020030.2960.4080.15020040.4290.5730.16020050.5470.6850.47020060.6570.7530.65020070.7450.8100.58020080.8210.8420.40020090.8500.8450.540Figure 8
Simulation results and errors of ocean GDP from 2001 to 2009.It can be seen from Table3 and Figure 8 that Fujian keeps one place in the influence factor of the proportion of ocean economy, which is a direct reflection of the high proportion of ocean economy to GDP in the Annex. The current value added of the Zhoushan ocean economy accounts for more than 70% of GDP, but the effect of this factor on the operation of the ocean economy is not obvious.


## 5. Conclusion

The former Fujian Province has a number of national policies on the ocean economy, such as the Pilot-Free Trade Zone and the Jianghai Intermodal Transportation Service Center, shouldering the exemplary role of transforming the ocean economy and improving the degree of openness. Finding strategies to enhance the development of the ocean economy is a need for the Fujian Province and the entire country. This paper aims to provide a more comprehensive analysis of the current state of the ocean economy in the Fujian Province by conducting a qualitative analysis of the overall ocean economy and the development status of each industry in the Fujian Province, as well as evaluating the efficiency of the ocean economy development in the Fujian Province longitudinally using data envelopment analysis. Furthermore, comparing other coastal cities using exploratory factor analysis to find out the problems that restrict its development in conjunction with the actual economic significance and seeking practical policy suggestions and feasible solutions to the problems, this paper makes predictions on the ocean production output value of the Fujian Province by building a deep neural network. The experimental findings suggest that the model proposed in this research is capable of accurately predicting the Fujian Province’s ocean production value. This work still has several flaws, primarily because, as a broad science, ocean economics encompasses a wide range of topics, and the research infrastructure is complex. A neural network model is a continuous learning model based on training samples, and a network based on a small number of training samples can only be used for short-term prediction. For long-term prediction, the network must be trained with more data. The model presented in this article is also only useful for short-term prediction.



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*Source: 2186866-2022-05-07.xml*