# Development Path and Mechanism of Cultural Tourism Integration Based on Deep Learning Mobile Big Data Analysis

Authors: Renfei Zhang; Yang Bai
Journal: Journal of Electrical and Computer Engineering
 (2022)

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## Abstract

Under the current situation, there are many drawbacks in the integrated development of culture and tourism. Therefore, this paper aims to use big data analysis to study the path and development mechanism of cultural and tourism integration, aiming to explore a road of cultural and tourism integration that is most suitable for domestic development. This paper proposes a big data platform based on deep learning to adapt to domestic cultural data and tourism data. The system can intuitively analyze the domestic cultural tourism industry and display the development of cultural tourism with brief pictures and figures. This article analyzes the performance of the designed system and tests the fusion of cultural tourism data. The experimental results of this paper prove that the stability design of the big data cultural tourism industry analysis platform in this paper meets the requirements, and the platform can be stable at 5 Mb/s for 40–180 G big data transmission. The results of the modeling analysis of the integrated development of cultural tourism can be considered; that is, the cultural tourism industry has made great progress in the process of integration, and the integrated development of these two industries is imperative.


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## Body

## 1. Introduction

Under the background of the healthy development and continuous improvement of China’s social economy and the establishment of a moderately prosperous society in an all-round way and the reality that the per capita disposable income of the whole Chinese people is constantly increasing, as the Chinese society is more and more closely contacted with the world, the people’s interest in the comprehensive needs and requirements of cultural tourism are constantly improving. The integrated development of the cultural industry and the tourism industry is also adapting to the growing demands of the times and the market. With the healthy development and continuous growth of the domestic economy and society, the increasing living conditions and spiritual needs of the people, and the rapid development and continuous improvement of domestic and global transportation networks, the tourism industry has developed into an important strategic industry in the domestic social economy and the development prospects are limitless. As the only country in the world with a continuous civilization of 5,000 years, China has various cultures blooming everywhere, and cultures of different types and ages coexist. China makes the unique culture distributed in the county-level cities shine again and then integrates it into the development of global tourism to continuously provide impetus for economic and social development. To a certain extent, the cultural tourism routes and cultural tourism brands that link the 5,000-year-old Chinese civilization are formed by connecting the dots. It also promotes the inheritance of traditional culture and enhances national cultural confidence to a certain extent. Therefore, in the transition period of the new era, it is necessary to study the development of cultural and tourism integration, especially for the integrated analysis of big data, which is very helpful to study its development path.For the research of this paper, the main contributions are as follows: (1) Design a big data platform for the integration of culture and tourism, which can well communicate the data resources of culture and tourism, and simulate the development of cultural and tourism integration. (2) Quantitative analysis was carried out on the research on culture and tourism industry, which fully explained the development of related industries.In this paper, the research on the development of cultural and tourism integration is mainly aimed at the research of big data. Compared with the predecessors, there are the following two innovations: (1) The design of the big data platform is proposed for the integrated development of culture and tourism, which has strong practical significance for the intelligentization of the integrated development of culture and tourism and the subsequent sustainable development. (2) In this paper, the model of cultural tourism industry development is studied. Through quantitative analysis, the degree of development is graded according to the calculation results so that it can clearly see the development of related industries, strengthen their weaknesses, and maintain their strengths, thereby enhancing the overall development level of cultural tourism integration.


## 2. Related Work

In the process of tourism and cultural industry transformation and upgrading in the new era, the support of deep learning and big data platforms is indispensable. The following content is a brief introduction to related research on cultural and tourism integration, deep learning, and big data platforms. Liao believes that local color is not only a cultural counter in urban competition, but also a major cultural element that attracts foreign tourists. A fusion analysis of rural culture and tourism based on the local color was conducted, and after research, it is believed that the ancient town should be fully protected [1]. Choo and others believe that in Cambodia, the rapid development of tourism has become one of the main forces driving the economy. Focusing on cultural and heritage tourism in the Siem Reap region, they analyzed Cambodia’s tourism competitiveness and tried to find ways to strengthen it [2]. The research on deep learning has always been enthusiastic, not only in a wide range of applications, but also in many functions. Chen et al. conducted a study on one of the hottest topics in hyperspectral remote sensing, classification of hyperspectral remote sensing. They proposed a novel deep learning framework to incorporate features to obtain the highest classification accuracy [3]. Kermany et al. established a deep learning framework-based diagnostic tool for screening patients with common treatable blinding retinal diseases. Their experiments suggest that the tool may ultimately help to speed up the diagnosis and referral of these treatable diseases, thereby facilitating early treatment and thus improving clinical outcomes [4]. Several new applications of deep learning at the physical layer are shown and discussed by Oshea and Hoydis. In their study, they demonstrated the application of convolutional neural networks on raw IQ samples for modulation classification, achieving competitive accuracy compared to traditional schemes relying on expert features [5]. Ravi et al. mainly focus on key applications of deep learning in translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health [6]. Many applications of deep learning are inseparable from the support of big data. Zhao et al. designed a powerful big data monitoring platform for big data technology. After experimental verification, they believe that the platform they proposed can meet the design requirements [7]. Sun and Zhang conducted research on the role of smart big data platform and selected the development of smart city in Hefei as an empirical analysis. From the connotation of smart city and the combination of blockchain and big data technology, this analysis summarizes the positive effect of relevant information technology on the construction of smart city big data platform [8].After the introduction, it can be found that there are not many digital studies on the integration of culture and tourism, and more are biased towards the analysis of the cultural industry and the development of the tourism industry. There are few documents that comprehensively analyze the digitalization of the integration of culture and tourism. Therefore, the focus of this paper is to conduct a more in-depth study on the development path and development mechanism of the digital development after the integration of the cultural tourism industry and provide the design scheme of the big data platform. Different from the related research, this paper pays more attention to the database design of the system and also puts forward new requirements for the platform architecture.


## 3. Integration of Big Data and Cultural Tourism

### 3.1. Cultural and Tourism Integration

#### 3.1.1. Cultural Industry

The word “culture” appeared as early as in the “Book of Changes,” “viewing the humanities and transforming them into the world” includes the meaning of “culture.” At this time, “culture” is used as a verb. In the twentieth century, the word “culture” was influenced by foreign countries and became an imported concept; it has changed from a verb to a noun, referring to all the creations of human beings in the development of society, including spiritual culture and material culture [9,10]. The cultural industry first emerged in the United States in the middle of the twentieth century, and the United States has transformed from a small country of cultural resources to a large cultural exporter. The competition between countries includes not only “hard power,” but also “soft power” competition, and the cultural industry is an important part of the competition [11]. In the early twenty-first century, China gave a positive value judgment to the cultural industry. With the improvement of comprehensive national strength, the development policy and institutional reform of the cultural industry have been gradually improved and deepened, and a large number of academic works and theories on the cultural industry have emerged [12]. Different countries have different definitions of cultural industries [13]. In 1986, UNESCO defined cultural industries as activities and outputs based on cultural and artistic creative expressions, heritage, and monuments, including social and cultural activities, cultural heritage, music, performing arts, visual arts, writings and documents, audiovisual media, audio media, sports and games, environment and nature, and other ten categories. On this basis, countries formulate standards and concepts suitable for their own national conditions. The domestic cultural industry defines it as a collection of production and business activities for cultural products and other cultural-related products needed by the public. Its industry boundaries are shown in Figure 1.Figure 1
Industrial boundaries of cultural industries.


#### 3.1.2. Tourism Industry

From the perspective of tourism industry geography, tourism is an intermediate experience link between geographical landscape and geographical culture. Under the influence of cultural popularization, it can give tourists an experience sublimation from the physical geographical landscape to the cultural level [14]. The tourism industry is regarded as a detonating industry; that is, tourism can play an explosive economic development role in the development of the national economy. This is reflected in the fact that the income of the tourism sector can increase by 5 to 7 yuan for the entire society, including the service industry and personal income [15]. Domestically, tourism is classified as the tertiary industry, and some Western countries regard it as the fifth industry. The fifth industry is an industry that appears to meet the purpose of leisure services when a large amount of leisure time increases. Some scholars call it the recreational ethic industry. This difference is mainly due to the fact that the definition, composition, and division of the tourism industry at home and abroad have not yet reached a unified understanding [16,17]. This paper argues that the tourism industry is for the purpose of satisfying the increasing spiritual and cultural consumption of tourists, relying on tourism resources to drive the agglomeration of industry service groups related to “eating, housing, traveling, traveling, shopping, and entertainment.” Regarding the composition of the tourism industry, some researchers have summarized the representative viewpoints as pillar industry theory, element theory, industrial group theory, hierarchy theory, and industrial cluster theory. Its industry boundaries are shown in Figure 2.Figure 2
Industry boundary of tourism industry.


#### 3.1.3. Integration of Culture and Tourism

The research perspective of industrial integration is different, and scholars have different definitions for it, mainly from the perspective of technology integration, fuzzy development of industrial boundaries, and system coupling theory [18].The integration of cultural tourism industry should be developed from the perspective of cultural characteristics, cultural industry chain, cultural creative products, and other cultural elements infiltration and integration of tourism elements [19]. The integration of cultural tourism is the result of the internal and external extension and mutual penetration of the cultural industry and its derivatives and the tourism industry [20]. The basic form of its integration is to dig deep into the cultural connotation contained in cultural resources and express and interpret its representative cultural elements in a reasonable display form so as to attract and meet the needs of visitors for viewing, participating, leisure, entertainment, or cultivating sentiments [21]. Effectively integrate cultural resources and their derivatives go into the tourism industry. Cultural elements enhance the connotation of tourism; excellent tourism products promote the development of cultural industries and at the same time expand cultural consumption and dissemination.


### 3.2. Deep Learning and Machine Learning

With the deep learning-based Alpha Go program developed by Google-owned Company DeepMind defeating professional Go players, the topic of artificial intelligence has become hot again. This is the strongest response since the trough of artificial intelligence in the 1980s, and many people even call the current era the era of intelligence [22]. Deep learning and machine learning are the main means to realize artificial intelligence [23].The relationship between artificial intelligence, machine learning, and deep learning can be represented by Figure3. As shown in the figure, artificial intelligence, abbreviated as AI in English, is a branch of computing science. Its main research goal is to simulate the thinking process of human beings so that computers can think and solve problems like humans and learn or acquire knowledge or skills that they do not possess.Figure 3
Artificial intelligence, machine learning, and deep learning.Machine Learning (ML) is used to study how to make computers think like humans, solve problems, and learn or acquire knowledge or skills that they do not possess [24]. It is a science and technology that uses existing experience and technology to continuously adjust and improve its own performance or knowledge structure. It is an interdisciplinary subject of computer science and technology, mathematical analysis, applied calculus, information theory, and probability and statistics. Machine learning analyzes past historical data by setting corresponding algorithms for research goals and establishes corresponding mathematical models according to the analysis results [25, 26]. It can then continuously improve and optimize its own performance while transforming the messy data into valid results by continuously inputting data to the established mathematical model. Machine learning can be divided into two categories according to methods: supervised learning and unsupervised learning. If there are samples in the learning process of building a model, this type of learning is called supervised learning, and this type of learning is mainly composed of classification and regression problems. If there are no samples, people can try to let the computer learn and generalize possible behavior categories by itself. This kind of learning is usually called unsupervised learning and mainly consists of problems such as clustering and association analysis. The machine learning problem solving process is shown in Figure 4.Figure 4
Machine learning problem solving flowchart.The research purpose of deep learning is the same as that of machine learning. It is hoped that through learning and training, computers can have the ability to analyze problems, solve problems, and optimize themselves like the human brain. It is the enhancement of artificial neural network in machine learning, which enables computers to better mine potential rules and internal rules between complex data. It solves many complex recognition problems and is a milestone in the research of artificial intelligence related technologies. Combining deep learning techniques with various practical applications is a major current research trend. Today, deep learning has achieved many research results in translation search, speech recognition, robotics, pattern recognition, natural speech processing, network media technology, recommender systems, and many other fields. Particularly in the fields of speech recognition and image recognition, the achievements of deep learning in these two fields far exceed other related technologies. The flowchart of deep learning problem solving is shown in Figure5.Figure 5
Flowchart of deep learning problem solving.Figure5 is a flowchart of deep learning problem solving. Deep learning will automatically filter the research data and extract the complex features of the data, without the need for people to manually label the data features. Compared with the supervised learning method in traditional machine learning, this saves researchers a lot of research time.


## 3.1. Cultural and Tourism Integration

### 3.1.1. Cultural Industry

The word “culture” appeared as early as in the “Book of Changes,” “viewing the humanities and transforming them into the world” includes the meaning of “culture.” At this time, “culture” is used as a verb. In the twentieth century, the word “culture” was influenced by foreign countries and became an imported concept; it has changed from a verb to a noun, referring to all the creations of human beings in the development of society, including spiritual culture and material culture [9,10]. The cultural industry first emerged in the United States in the middle of the twentieth century, and the United States has transformed from a small country of cultural resources to a large cultural exporter. The competition between countries includes not only “hard power,” but also “soft power” competition, and the cultural industry is an important part of the competition [11]. In the early twenty-first century, China gave a positive value judgment to the cultural industry. With the improvement of comprehensive national strength, the development policy and institutional reform of the cultural industry have been gradually improved and deepened, and a large number of academic works and theories on the cultural industry have emerged [12]. Different countries have different definitions of cultural industries [13]. In 1986, UNESCO defined cultural industries as activities and outputs based on cultural and artistic creative expressions, heritage, and monuments, including social and cultural activities, cultural heritage, music, performing arts, visual arts, writings and documents, audiovisual media, audio media, sports and games, environment and nature, and other ten categories. On this basis, countries formulate standards and concepts suitable for their own national conditions. The domestic cultural industry defines it as a collection of production and business activities for cultural products and other cultural-related products needed by the public. Its industry boundaries are shown in Figure 1.Figure 1
Industrial boundaries of cultural industries.


### 3.1.2. Tourism Industry

From the perspective of tourism industry geography, tourism is an intermediate experience link between geographical landscape and geographical culture. Under the influence of cultural popularization, it can give tourists an experience sublimation from the physical geographical landscape to the cultural level [14]. The tourism industry is regarded as a detonating industry; that is, tourism can play an explosive economic development role in the development of the national economy. This is reflected in the fact that the income of the tourism sector can increase by 5 to 7 yuan for the entire society, including the service industry and personal income [15]. Domestically, tourism is classified as the tertiary industry, and some Western countries regard it as the fifth industry. The fifth industry is an industry that appears to meet the purpose of leisure services when a large amount of leisure time increases. Some scholars call it the recreational ethic industry. This difference is mainly due to the fact that the definition, composition, and division of the tourism industry at home and abroad have not yet reached a unified understanding [16,17]. This paper argues that the tourism industry is for the purpose of satisfying the increasing spiritual and cultural consumption of tourists, relying on tourism resources to drive the agglomeration of industry service groups related to “eating, housing, traveling, traveling, shopping, and entertainment.” Regarding the composition of the tourism industry, some researchers have summarized the representative viewpoints as pillar industry theory, element theory, industrial group theory, hierarchy theory, and industrial cluster theory. Its industry boundaries are shown in Figure 2.Figure 2
Industry boundary of tourism industry.


### 3.1.3. Integration of Culture and Tourism

The research perspective of industrial integration is different, and scholars have different definitions for it, mainly from the perspective of technology integration, fuzzy development of industrial boundaries, and system coupling theory [18].The integration of cultural tourism industry should be developed from the perspective of cultural characteristics, cultural industry chain, cultural creative products, and other cultural elements infiltration and integration of tourism elements [19]. The integration of cultural tourism is the result of the internal and external extension and mutual penetration of the cultural industry and its derivatives and the tourism industry [20]. The basic form of its integration is to dig deep into the cultural connotation contained in cultural resources and express and interpret its representative cultural elements in a reasonable display form so as to attract and meet the needs of visitors for viewing, participating, leisure, entertainment, or cultivating sentiments [21]. Effectively integrate cultural resources and their derivatives go into the tourism industry. Cultural elements enhance the connotation of tourism; excellent tourism products promote the development of cultural industries and at the same time expand cultural consumption and dissemination.


## 3.1.1. Cultural Industry

The word “culture” appeared as early as in the “Book of Changes,” “viewing the humanities and transforming them into the world” includes the meaning of “culture.” At this time, “culture” is used as a verb. In the twentieth century, the word “culture” was influenced by foreign countries and became an imported concept; it has changed from a verb to a noun, referring to all the creations of human beings in the development of society, including spiritual culture and material culture [9,10]. The cultural industry first emerged in the United States in the middle of the twentieth century, and the United States has transformed from a small country of cultural resources to a large cultural exporter. The competition between countries includes not only “hard power,” but also “soft power” competition, and the cultural industry is an important part of the competition [11]. In the early twenty-first century, China gave a positive value judgment to the cultural industry. With the improvement of comprehensive national strength, the development policy and institutional reform of the cultural industry have been gradually improved and deepened, and a large number of academic works and theories on the cultural industry have emerged [12]. Different countries have different definitions of cultural industries [13]. In 1986, UNESCO defined cultural industries as activities and outputs based on cultural and artistic creative expressions, heritage, and monuments, including social and cultural activities, cultural heritage, music, performing arts, visual arts, writings and documents, audiovisual media, audio media, sports and games, environment and nature, and other ten categories. On this basis, countries formulate standards and concepts suitable for their own national conditions. The domestic cultural industry defines it as a collection of production and business activities for cultural products and other cultural-related products needed by the public. Its industry boundaries are shown in Figure 1.Figure 1
Industrial boundaries of cultural industries.


## 3.1.2. Tourism Industry

From the perspective of tourism industry geography, tourism is an intermediate experience link between geographical landscape and geographical culture. Under the influence of cultural popularization, it can give tourists an experience sublimation from the physical geographical landscape to the cultural level [14]. The tourism industry is regarded as a detonating industry; that is, tourism can play an explosive economic development role in the development of the national economy. This is reflected in the fact that the income of the tourism sector can increase by 5 to 7 yuan for the entire society, including the service industry and personal income [15]. Domestically, tourism is classified as the tertiary industry, and some Western countries regard it as the fifth industry. The fifth industry is an industry that appears to meet the purpose of leisure services when a large amount of leisure time increases. Some scholars call it the recreational ethic industry. This difference is mainly due to the fact that the definition, composition, and division of the tourism industry at home and abroad have not yet reached a unified understanding [16,17]. This paper argues that the tourism industry is for the purpose of satisfying the increasing spiritual and cultural consumption of tourists, relying on tourism resources to drive the agglomeration of industry service groups related to “eating, housing, traveling, traveling, shopping, and entertainment.” Regarding the composition of the tourism industry, some researchers have summarized the representative viewpoints as pillar industry theory, element theory, industrial group theory, hierarchy theory, and industrial cluster theory. Its industry boundaries are shown in Figure 2.Figure 2
Industry boundary of tourism industry.


## 3.1.3. Integration of Culture and Tourism

The research perspective of industrial integration is different, and scholars have different definitions for it, mainly from the perspective of technology integration, fuzzy development of industrial boundaries, and system coupling theory [18].The integration of cultural tourism industry should be developed from the perspective of cultural characteristics, cultural industry chain, cultural creative products, and other cultural elements infiltration and integration of tourism elements [19]. The integration of cultural tourism is the result of the internal and external extension and mutual penetration of the cultural industry and its derivatives and the tourism industry [20]. The basic form of its integration is to dig deep into the cultural connotation contained in cultural resources and express and interpret its representative cultural elements in a reasonable display form so as to attract and meet the needs of visitors for viewing, participating, leisure, entertainment, or cultivating sentiments [21]. Effectively integrate cultural resources and their derivatives go into the tourism industry. Cultural elements enhance the connotation of tourism; excellent tourism products promote the development of cultural industries and at the same time expand cultural consumption and dissemination.


## 3.2. Deep Learning and Machine Learning

With the deep learning-based Alpha Go program developed by Google-owned Company DeepMind defeating professional Go players, the topic of artificial intelligence has become hot again. This is the strongest response since the trough of artificial intelligence in the 1980s, and many people even call the current era the era of intelligence [22]. Deep learning and machine learning are the main means to realize artificial intelligence [23].The relationship between artificial intelligence, machine learning, and deep learning can be represented by Figure3. As shown in the figure, artificial intelligence, abbreviated as AI in English, is a branch of computing science. Its main research goal is to simulate the thinking process of human beings so that computers can think and solve problems like humans and learn or acquire knowledge or skills that they do not possess.Figure 3
Artificial intelligence, machine learning, and deep learning.Machine Learning (ML) is used to study how to make computers think like humans, solve problems, and learn or acquire knowledge or skills that they do not possess [24]. It is a science and technology that uses existing experience and technology to continuously adjust and improve its own performance or knowledge structure. It is an interdisciplinary subject of computer science and technology, mathematical analysis, applied calculus, information theory, and probability and statistics. Machine learning analyzes past historical data by setting corresponding algorithms for research goals and establishes corresponding mathematical models according to the analysis results [25, 26]. It can then continuously improve and optimize its own performance while transforming the messy data into valid results by continuously inputting data to the established mathematical model. Machine learning can be divided into two categories according to methods: supervised learning and unsupervised learning. If there are samples in the learning process of building a model, this type of learning is called supervised learning, and this type of learning is mainly composed of classification and regression problems. If there are no samples, people can try to let the computer learn and generalize possible behavior categories by itself. This kind of learning is usually called unsupervised learning and mainly consists of problems such as clustering and association analysis. The machine learning problem solving process is shown in Figure 4.Figure 4
Machine learning problem solving flowchart.The research purpose of deep learning is the same as that of machine learning. It is hoped that through learning and training, computers can have the ability to analyze problems, solve problems, and optimize themselves like the human brain. It is the enhancement of artificial neural network in machine learning, which enables computers to better mine potential rules and internal rules between complex data. It solves many complex recognition problems and is a milestone in the research of artificial intelligence related technologies. Combining deep learning techniques with various practical applications is a major current research trend. Today, deep learning has achieved many research results in translation search, speech recognition, robotics, pattern recognition, natural speech processing, network media technology, recommender systems, and many other fields. Particularly in the fields of speech recognition and image recognition, the achievements of deep learning in these two fields far exceed other related technologies. The flowchart of deep learning problem solving is shown in Figure5.Figure 5
Flowchart of deep learning problem solving.Figure5 is a flowchart of deep learning problem solving. Deep learning will automatically filter the research data and extract the complex features of the data, without the need for people to manually label the data features. Compared with the supervised learning method in traditional machine learning, this saves researchers a lot of research time.


## 4. Big Data Platform for Cultural and Tourism Integration

### 4.1. Demand Analysis

The cultural and tourism integration big data platform implemented in this paper refers to the classic big data platform, focusing on the generation, scheduling, execution, and other operations of the entire job, aiming to obtain data that is close to the real. The following factors need to be considered when implementing these functions: (1) The parameter setting of the job scheduling simulation system needs to be flexible, leaving a configuration interface for the user to easily adjust the configuration of the system so that it can be applied to other big data platforms later, and at the same time, it is convenient for users to modify. (2) The method of job generation considers the method of random generation because the online job scheduling situation in the real environment does not know the resource requirements and duration of specific jobs, so this paper considers the method of random job generation to simulate online job scheduling. And the setting of the job must conform to the real environment and cannot exceed the maximum number of system resources. (3) In addition to the deep reinforcement learning algorithm in this paper, other commonly used algorithms, such as short assignment priority and packer algorithm, need to be considered for subsequent comparison tests so as to obtain the performance advantages of the deep reinforcement learning algorithm. (4) It is necessary to consider the portability of the simulation system and try not to use non-open-source frameworks and codes to ensure subsequent secondary development. Specifically, the functions that the entire system needs to implement include randomly generating desired job batches, complete job scheduling process, complete job execution process, and result testing and display functions [27, 28]. However, the whole system is still in the development state, and the functions are relatively simple. The use case diagram of the whole system is shown in Figure 6.Figure 6
System use case.As shown in Figure6, what the user can perform are system configuration settings, algorithm settings, model training, and display of test results. Specifically, the user needs to personalize the specific configuration of the entire system and then set the standard algorithms, such as short-job priority, fixed algorithms such as packer, or trained deep reinforcement learning algorithms. The third is model training. Users may iterate and improve the algorithm according to their own needs until they reach their expected goals and finally display the test results.After the algorithm is selected, a set of data can be randomly generated, and the entire scheduling process can be run according to the algorithm, and the final evaluation index average slowdown and other indicators can be displayed, which is convenient for comparison experiments later. At the same time, for the system performance requirements, the first is that the execution time in the process of model invocation should not be too high, and then the situation of system stability, and there should be no job deadlock or infinite loop.


### 4.2. System Design

The big data platform includes the whole process of big data processing [29]. The overall architecture of the platform is shown in Figure 7.Figure 7
Overall architecture of the big data platform.Figure7 shows that the platform is divided into three layers: physical, platform, and application. First of all, the platform layer, because the platform layer involves many components and modules, mainly introduces the more important parts. Because the management system is mainly designed for the communication big data platform, the following is a detailed introduction to the big data platform.


#### 4.2.1. Platform Layer

First, the data comes from the data warehouse architecture, specifically the db2 local data interface. After that, data is collected and extracted through swoop and flume components, which can be summarized and divided according to dimensions. After uploading to huffs, perform related ETL operations again, such as code value conversion and merge. There are two or more components for caching massive data, namely, data storage components such as HBase and kafka in the platform layer. Because the location of HBase is the storage layer, HDFS is a distributed storage system to provide the underlying storage, and MapReduce is an efficient distributed computing framework. Zookeeper is the communication service and failover mechanism. Kafka meets the needs of log processing and high throughput. Zookeeper is used to allocate cluster resources and is responsible for communication between components. The scheduling module is convenient for people to manage the running status of the ETL program and shell script in the data warehouse and can understand the running status of the program at the same time. The last is the algorithm analysis module, including traditional machine learning module and deep learning module.


#### 4.2.2. Physical Layer

Because the platform layer is divided into the production environment and the test environment, the equipment of the physical layer is also divided into two types: production and test, which are used by the production big data system and the experimental system. The physical devices are classified to isolate the two different usage scenarios of production and experimentation at the bottom layer so that the computing resources and storage resources dedicated to production services and production applications will not be occupied by the experimental environment. The experimental environment also has its own resources so as to achieve the effect of performance isolation. This article mainly discusses the application of deep learning models on the big data platform and how to interact with the big data management system modules.


#### 4.2.3. Application Layer

The physical layer mainly covers servers, switches, disk arrays, and so on, providing services for the above applications. Production applications, data experiment applications, and test applications are the use of the application layer. The support of big data systems is mainly provided by production applications, and data experiment applications and test applications are mainly provided by virtualization.


### 4.3. System Test

We test the performance of the dual system on the software. The intermediate information in the figure includes the cumulative reward obtained by each choice at each step and the maximum reward action obtained at the current time step in the current training. Then, this information is used as the data for the next round of training to train again. The curve obtained in the case of background data loss is shown in Figure8.Figure 8
Multiresource pool training loss.It can be seen from Figure8 that although there are fluctuations, the loss is gradually decreasing, indicating that the network tends to fit. After a certain round of training, the network can be tested after a certain convergence.As shown in Table1, the basic migration speed of the system remains between 2.5 MB and 6 MB. When writing to HBase, the speed is faster because HBase takes into account some concurrency designs. Because this migration test is mainly based on offline data migration, this speed can be basically satisfied based on past production experience.Table 1
Data migration function effect test.
Data sourceData destinationData volume (GB)Migration time (h)Average speedMySQLHDFS18010.564.85 MB/sHDFSHBase1205.965.73 MB/sMySQLHBase402.354.85 MB/sHBaseMySQL908.682.95 MB/sKafkaRedis603.035.64 MB/s


## 4.1. Demand Analysis

The cultural and tourism integration big data platform implemented in this paper refers to the classic big data platform, focusing on the generation, scheduling, execution, and other operations of the entire job, aiming to obtain data that is close to the real. The following factors need to be considered when implementing these functions: (1) The parameter setting of the job scheduling simulation system needs to be flexible, leaving a configuration interface for the user to easily adjust the configuration of the system so that it can be applied to other big data platforms later, and at the same time, it is convenient for users to modify. (2) The method of job generation considers the method of random generation because the online job scheduling situation in the real environment does not know the resource requirements and duration of specific jobs, so this paper considers the method of random job generation to simulate online job scheduling. And the setting of the job must conform to the real environment and cannot exceed the maximum number of system resources. (3) In addition to the deep reinforcement learning algorithm in this paper, other commonly used algorithms, such as short assignment priority and packer algorithm, need to be considered for subsequent comparison tests so as to obtain the performance advantages of the deep reinforcement learning algorithm. (4) It is necessary to consider the portability of the simulation system and try not to use non-open-source frameworks and codes to ensure subsequent secondary development. Specifically, the functions that the entire system needs to implement include randomly generating desired job batches, complete job scheduling process, complete job execution process, and result testing and display functions [27, 28]. However, the whole system is still in the development state, and the functions are relatively simple. The use case diagram of the whole system is shown in Figure 6.Figure 6
System use case.As shown in Figure6, what the user can perform are system configuration settings, algorithm settings, model training, and display of test results. Specifically, the user needs to personalize the specific configuration of the entire system and then set the standard algorithms, such as short-job priority, fixed algorithms such as packer, or trained deep reinforcement learning algorithms. The third is model training. Users may iterate and improve the algorithm according to their own needs until they reach their expected goals and finally display the test results.After the algorithm is selected, a set of data can be randomly generated, and the entire scheduling process can be run according to the algorithm, and the final evaluation index average slowdown and other indicators can be displayed, which is convenient for comparison experiments later. At the same time, for the system performance requirements, the first is that the execution time in the process of model invocation should not be too high, and then the situation of system stability, and there should be no job deadlock or infinite loop.


## 4.2. System Design

The big data platform includes the whole process of big data processing [29]. The overall architecture of the platform is shown in Figure 7.Figure 7
Overall architecture of the big data platform.Figure7 shows that the platform is divided into three layers: physical, platform, and application. First of all, the platform layer, because the platform layer involves many components and modules, mainly introduces the more important parts. Because the management system is mainly designed for the communication big data platform, the following is a detailed introduction to the big data platform.


### 4.2.1. Platform Layer

First, the data comes from the data warehouse architecture, specifically the db2 local data interface. After that, data is collected and extracted through swoop and flume components, which can be summarized and divided according to dimensions. After uploading to huffs, perform related ETL operations again, such as code value conversion and merge. There are two or more components for caching massive data, namely, data storage components such as HBase and kafka in the platform layer. Because the location of HBase is the storage layer, HDFS is a distributed storage system to provide the underlying storage, and MapReduce is an efficient distributed computing framework. Zookeeper is the communication service and failover mechanism. Kafka meets the needs of log processing and high throughput. Zookeeper is used to allocate cluster resources and is responsible for communication between components. The scheduling module is convenient for people to manage the running status of the ETL program and shell script in the data warehouse and can understand the running status of the program at the same time. The last is the algorithm analysis module, including traditional machine learning module and deep learning module.


### 4.2.2. Physical Layer

Because the platform layer is divided into the production environment and the test environment, the equipment of the physical layer is also divided into two types: production and test, which are used by the production big data system and the experimental system. The physical devices are classified to isolate the two different usage scenarios of production and experimentation at the bottom layer so that the computing resources and storage resources dedicated to production services and production applications will not be occupied by the experimental environment. The experimental environment also has its own resources so as to achieve the effect of performance isolation. This article mainly discusses the application of deep learning models on the big data platform and how to interact with the big data management system modules.


### 4.2.3. Application Layer

The physical layer mainly covers servers, switches, disk arrays, and so on, providing services for the above applications. Production applications, data experiment applications, and test applications are the use of the application layer. The support of big data systems is mainly provided by production applications, and data experiment applications and test applications are mainly provided by virtualization.


## 4.2.1. Platform Layer

First, the data comes from the data warehouse architecture, specifically the db2 local data interface. After that, data is collected and extracted through swoop and flume components, which can be summarized and divided according to dimensions. After uploading to huffs, perform related ETL operations again, such as code value conversion and merge. There are two or more components for caching massive data, namely, data storage components such as HBase and kafka in the platform layer. Because the location of HBase is the storage layer, HDFS is a distributed storage system to provide the underlying storage, and MapReduce is an efficient distributed computing framework. Zookeeper is the communication service and failover mechanism. Kafka meets the needs of log processing and high throughput. Zookeeper is used to allocate cluster resources and is responsible for communication between components. The scheduling module is convenient for people to manage the running status of the ETL program and shell script in the data warehouse and can understand the running status of the program at the same time. The last is the algorithm analysis module, including traditional machine learning module and deep learning module.


## 4.2.2. Physical Layer

Because the platform layer is divided into the production environment and the test environment, the equipment of the physical layer is also divided into two types: production and test, which are used by the production big data system and the experimental system. The physical devices are classified to isolate the two different usage scenarios of production and experimentation at the bottom layer so that the computing resources and storage resources dedicated to production services and production applications will not be occupied by the experimental environment. The experimental environment also has its own resources so as to achieve the effect of performance isolation. This article mainly discusses the application of deep learning models on the big data platform and how to interact with the big data management system modules.


## 4.2.3. Application Layer

The physical layer mainly covers servers, switches, disk arrays, and so on, providing services for the above applications. Production applications, data experiment applications, and test applications are the use of the application layer. The support of big data systems is mainly provided by production applications, and data experiment applications and test applications are mainly provided by virtualization.


## 4.3. System Test

We test the performance of the dual system on the software. The intermediate information in the figure includes the cumulative reward obtained by each choice at each step and the maximum reward action obtained at the current time step in the current training. Then, this information is used as the data for the next round of training to train again. The curve obtained in the case of background data loss is shown in Figure8.Figure 8
Multiresource pool training loss.It can be seen from Figure8 that although there are fluctuations, the loss is gradually decreasing, indicating that the network tends to fit. After a certain round of training, the network can be tested after a certain convergence.As shown in Table1, the basic migration speed of the system remains between 2.5 MB and 6 MB. When writing to HBase, the speed is faster because HBase takes into account some concurrency designs. Because this migration test is mainly based on offline data migration, this speed can be basically satisfied based on past production experience.Table 1
Data migration function effect test.
Data sourceData destinationData volume (GB)Migration time (h)Average speedMySQLHDFS18010.564.85 MB/sHDFSHBase1205.965.73 MB/sMySQLHBase402.354.85 MB/sHBaseMySQL908.682.95 MB/sKafkaRedis603.035.64 MB/s


## 5. Modeling and Analysis of Integrated Development of Culture and Tourism

The cultural and tourism industry has always had parts that intersect with each other in terms of industrial content. And the connection between them cannot be erased. At the same time, an important part of tourism includes the cultural and entertainment sector in the cultural industry. The connotation of culture promotes the long-term barrier-free development of tourism. In the new era, tourists are concerned with gratification on a spiritual and cultural level. The local characteristics of tourism can meet the needs of ordinary tourists, and some special local cultural forms can improve their influence. Businesses related to cultural tourism have built a proprietary platform for the communication and dissemination of local characteristics, further improving the integration of culture and tourism and empowering two key resources. The integrated model can explore specific parameters such as the integration and coordinated development of the two industries in a quantitative form so as to reflect that the integration of the cultural and tourism industries is effective.


### 5.1. Indicator Selection

From the perspective of industrial integration, this paper selects appropriate evaluation indicators according to operational principles, reform and innovation principles, and scientific principles, and establishes an evaluation index system for cultural and tourism integration. This paper constructs an indicator method for measuring the integration level of the cultural tourism industry through word frequency statistics and expert interviews. From the perspective of innovation, feasibility, and rationality, 20 relevant indicators that can play the most representative role in the cultural tourism industry are selected, as shown in Table2.Table 2
Indicator system for measuring the development level of cultural industry and tourism industry.
Serial numberEvaluation indicatorsUnit1Gross product created by culture and entertainmentBillion2The scale of culture and entertainment practitioners10,000 people3Number of cultural and entertainment unitsPiece4Number of seats in art performance venuesThousand5Audience size in art performance venues10,000 people6Number of mass cultural institutionsPiece7Number of public librariesPiece8The collection of books in public librariesTen thousand volumes (pieces)9Public library spendingMillion10Number of museumsPiece11Museum artifacts collectionPiece12Museum visitors10,000 people13Tourism revenue scaleBillion14Domestic tourism revenue scaleBillion15The scale of foreign exchange earnings from tourismTen thousand U.S. dollars16Number of domestic tourists10,000 people17Number of foreign tourists10,000 people18Number of travel agenciesPiece19Number of three-star and above hotelsPiece20Number of A-level scenic spotsPiece


### 5.2. Description of Related Algorithms

#### 5.2.1. Entropy Method to Determine Weight

In order to study the problem of the integrated development of cultural tourism, this paper divides the integrated development of cultural tourism into a three-layer structure according to the structure of large indicators, medium indicators, and small indicators. However, the data of these indicators come from different departments and units, and their importance in the system is also different, which requires a method to quantify these different data. Traditional methods cannot accurately obtain the required conclusions. The entropy value method used in this paper is a method of index weight according to the size of the index, and its steps can be divided into 7 steps:(1)
Use the matrix of the following formula to construct a data matrix, wherexij represents the specific value of the small index j in the large index i.(1)A=x11⋯x1m⋮⋱⋮xn1⋯xnm.(2)
Use the ratio of a certain index to the sum of the same index to deal with the nonnegativity of the value.(3)
Use the following formula to calculate the weight coefficient of each small index in the large index.(2)pij=xij∑i=1nxij,j=1,2,….(4)
Calculate its entropy for the index of thejth item, as shown in the following formula:(3)ej=−K∗i=∑npij∗lnpij.HereK requires a nonnegative number and ej requires a value greater than or equal to zero. The constant K is inseparable from the sample size m; then it is set, as in the following formula:(4)K=1lnn.Then,0≤ej≤1.(5)
Calculate the difference coefficient of different indicators: for an index, the larger the difference coefficient, the greater the effect and the smaller the entropy value.(6)
Use the following formula to obtain the weight of each indicator:(5)Wj=Gj∑j=1mGj,j=1,2,…,m,whereGj is expressed as follows:(6)G=j1−ej.The larger the obtainedGj is, the more important the indicator is.(7)
Calculate the comprehensive score of each major indicator, as shown in the following formula:(7)Si=∑j=1mWj∗pij,i,j=1,2,….


#### 5.2.2. Improved Entropy Method

(1)
Establish a matrix of initial data, as in the following formula:(8)X=xij.(2)
Standardize the data:The data in the indicator table comes from different departments, and the units used are also different, which must be adjusted. In this paper, formula9 and formula 10 are used to standardize the raw data of the indicators to achieve the purpose of quantification. The data to be processed must be a positive number and between 0 and 1 to serve the subsequent calculation. The specific operation is calculated according to the positive and negative classification. When the calculation is positive, use formula 9, and when the calculation is negative, use formula 10:(9)Vij=xij−xminxmax−xmin,(10)Vij=xmax−xijxmax−xmin.It is worth noting that scale normalization will bring a value of 0, which is not good for taking logarithms and calculating entropy values at a later stage. Therefore, using the method of minimum offset, setting all 0 values to 0.00001, which does not affect the final value, the entropy value can be calculated, and the number of points for comprehensive evaluation can be obtained.(3)
The entropy value is calculated as follows:(11)ej=−1lnm∑i=1mpijlnpij,wherepij is expressed as follows:(12)pij=Vij∑i=1mVij.Among them,Vij is the standardized data.(4)
Use the following formula to calculate the coefficient of difference of thejth index:(13)gj=1−ej.(5)
The weight calculation is as follows:(14)Wij=gij∑j=1ngij.(6)
Use the following formula to obtain the overall high-level development score for each subject:(15)Z=100×∑pij×wj.


#### 5.2.3. Fusion Degree Model

(16)Fii=1∼m,(17)Fjj=1∼n.Formulas (16) and (17) represent the number of internal industries. When n is greater than 2, the mathematical calculation of the integration degree of interaction between all departments in the industry with the number n is as follows:(18)Cn=nF1∗F2∗…∗FnF1∗F2∗…∗Fnn1/n.Lettingn = 2, the fusion degree model becomes as shown in the following formula:(19)C2=nF1∗F2F1∗F221/2,where F1, F2 represent various fusion factors and ∗ represents the fusion situation. This formula can measure the degree of integration between the cultural industry and the tourism industry in the integration link. It can be obtained from the formula: the larger the value of C2, the greater the degree of fusion.In order to clearly express the results of industry integration, according to the research of Gangmin et al., the scope and level of integration are divided, as shown in Table3.Table 3
Division of industrial integration evaluation index system.
DivisionFusion interval (D)Coordination levelUncoordinated0.000–0.099Extremely out of balance0.100–0.199Severely disordered0.200–0.299Moderately disordered0.300–0.399Mild disorder0.400–0.499On the verge of dysregulationCoordination0.500–0.599Barely coordinated0.600–0.699Initial coordination0.700–0.799Intermediate coordinator0.800–0.899Well coordinated0.900–0.999Excellent coordination


### 5.3. Calculation and Analysis of Cultural and Tourism Industry Integration

The relevant data are obtained from the statistical public statements and economic development announcement forms from 2010 to 2018, as shown in Table4.Table 4
Statistical table of cultural industry and tourism industry indicators from 2010 to 2018.
201020112012201320142015201620172018179.7114.2141.1154.7152.1173.4213.9252.61130.824.95.35.25.566.38.78.98328128225329332133433534235346.56.55.35.13.73.13.54.4355121.827.516.68.2712.213.47.263373383383383383373953953957213123192222232323810911356164218521896157817.9.11997.82293.797084.610908.916921.127852.7935112127.212685.114379.918386.8102429363634343434351135863186358172536476584156984243652444420146398513524131211211087103511321221123114041175168213610.4805.3141212281663204225053035371614575.9777100111201618198824273034371415277624564161345736587403887266124197130665144670166780.99554112561534218427189071976020708239811780.5123.2158.9176.8198.2131.4273.6302.4341.9182592902963393654094685306311911211110810311711198988220313242394950677679With the help of the calculation formula of the entropy method, the data in Table4 are brought into the formula for calculation, and the index weights of the cultural industry and tourism industry from 2010 to 2018 are obtained. The weight of the indicator represents the importance of the indicator in the model. The larger the weight, the higher the importance of the indicator, as shown in Figure 9.Figure 9
Evaluation indicators of cultural and tourism industry integration.The entropy algorithm is used to obtain the value of the weight and put this value into the fusion model for in-depth calculation to obtain the overall evaluation level (i.e.,F1 and F2) and the degree of fusion (D) involved in the cultural and tourism industry from 2010 to 2018, as shown in Table 5 and Figure 10.Table 5
Level of integration of cultural and tourism industries from 2010 to 2018.
yearsType of developmentCoordination level2010Tourism lagsModerate coordination2011Tourism lagsLight coordination2012Tourism lagsLight coordination2013Tourism lagsOn the verge of dysregulation2014Cultural lagLight coordination2015Cultural lagLight coordination2016Tourism lagsBarely coordinated2017Cultural lagBarely coordinated2018Tourism lagsPrimary coordinationFigure 10
Trend chart of the integrated development level of cultural industry and tourism industry from 2010 to 2018.Conclusions can be drawn from Table5 and Figure 10. From the perspective of development trends, from 2010 to 2013, 2016, and 2018, the progress of tourism was lower than that of culture. From 2014 to 2015 and 2017, it can be seen that the overall cultural development level is far behind its tourism development level. According to the degree of integration (D), in the years from 2010 to 2018, both showed a gradual growth trend. According to the data from 2010 to 2017, it is obvious that the degree of fusion developed from moderate dissonance to reluctance, and in 2018, the two achieved a state of initial fusion. According to the results, the cultural tourism industry has made great progress in the process of integration, and the integration and development of these two industries is imperative.


## 5.1. Indicator Selection

From the perspective of industrial integration, this paper selects appropriate evaluation indicators according to operational principles, reform and innovation principles, and scientific principles, and establishes an evaluation index system for cultural and tourism integration. This paper constructs an indicator method for measuring the integration level of the cultural tourism industry through word frequency statistics and expert interviews. From the perspective of innovation, feasibility, and rationality, 20 relevant indicators that can play the most representative role in the cultural tourism industry are selected, as shown in Table2.Table 2
Indicator system for measuring the development level of cultural industry and tourism industry.
Serial numberEvaluation indicatorsUnit1Gross product created by culture and entertainmentBillion2The scale of culture and entertainment practitioners10,000 people3Number of cultural and entertainment unitsPiece4Number of seats in art performance venuesThousand5Audience size in art performance venues10,000 people6Number of mass cultural institutionsPiece7Number of public librariesPiece8The collection of books in public librariesTen thousand volumes (pieces)9Public library spendingMillion10Number of museumsPiece11Museum artifacts collectionPiece12Museum visitors10,000 people13Tourism revenue scaleBillion14Domestic tourism revenue scaleBillion15The scale of foreign exchange earnings from tourismTen thousand U.S. dollars16Number of domestic tourists10,000 people17Number of foreign tourists10,000 people18Number of travel agenciesPiece19Number of three-star and above hotelsPiece20Number of A-level scenic spotsPiece


## 5.2. Description of Related Algorithms

### 5.2.1. Entropy Method to Determine Weight

In order to study the problem of the integrated development of cultural tourism, this paper divides the integrated development of cultural tourism into a three-layer structure according to the structure of large indicators, medium indicators, and small indicators. However, the data of these indicators come from different departments and units, and their importance in the system is also different, which requires a method to quantify these different data. Traditional methods cannot accurately obtain the required conclusions. The entropy value method used in this paper is a method of index weight according to the size of the index, and its steps can be divided into 7 steps:(1)
Use the matrix of the following formula to construct a data matrix, wherexij represents the specific value of the small index j in the large index i.(1)A=x11⋯x1m⋮⋱⋮xn1⋯xnm.(2)
Use the ratio of a certain index to the sum of the same index to deal with the nonnegativity of the value.(3)
Use the following formula to calculate the weight coefficient of each small index in the large index.(2)pij=xij∑i=1nxij,j=1,2,….(4)
Calculate its entropy for the index of thejth item, as shown in the following formula:(3)ej=−K∗i=∑npij∗lnpij.HereK requires a nonnegative number and ej requires a value greater than or equal to zero. The constant K is inseparable from the sample size m; then it is set, as in the following formula:(4)K=1lnn.Then,0≤ej≤1.(5)
Calculate the difference coefficient of different indicators: for an index, the larger the difference coefficient, the greater the effect and the smaller the entropy value.(6)
Use the following formula to obtain the weight of each indicator:(5)Wj=Gj∑j=1mGj,j=1,2,…,m,whereGj is expressed as follows:(6)G=j1−ej.The larger the obtainedGj is, the more important the indicator is.(7)
Calculate the comprehensive score of each major indicator, as shown in the following formula:(7)Si=∑j=1mWj∗pij,i,j=1,2,….


### 5.2.2. Improved Entropy Method

(1)
Establish a matrix of initial data, as in the following formula:(8)X=xij.(2)
Standardize the data:The data in the indicator table comes from different departments, and the units used are also different, which must be adjusted. In this paper, formula9 and formula 10 are used to standardize the raw data of the indicators to achieve the purpose of quantification. The data to be processed must be a positive number and between 0 and 1 to serve the subsequent calculation. The specific operation is calculated according to the positive and negative classification. When the calculation is positive, use formula 9, and when the calculation is negative, use formula 10:(9)Vij=xij−xminxmax−xmin,(10)Vij=xmax−xijxmax−xmin.It is worth noting that scale normalization will bring a value of 0, which is not good for taking logarithms and calculating entropy values at a later stage. Therefore, using the method of minimum offset, setting all 0 values to 0.00001, which does not affect the final value, the entropy value can be calculated, and the number of points for comprehensive evaluation can be obtained.(3)
The entropy value is calculated as follows:(11)ej=−1lnm∑i=1mpijlnpij,wherepij is expressed as follows:(12)pij=Vij∑i=1mVij.Among them,Vij is the standardized data.(4)
Use the following formula to calculate the coefficient of difference of thejth index:(13)gj=1−ej.(5)
The weight calculation is as follows:(14)Wij=gij∑j=1ngij.(6)
Use the following formula to obtain the overall high-level development score for each subject:(15)Z=100×∑pij×wj.


### 5.2.3. Fusion Degree Model

(16)Fii=1∼m,(17)Fjj=1∼n.Formulas (16) and (17) represent the number of internal industries. When n is greater than 2, the mathematical calculation of the integration degree of interaction between all departments in the industry with the number n is as follows:(18)Cn=nF1∗F2∗…∗FnF1∗F2∗…∗Fnn1/n.Lettingn = 2, the fusion degree model becomes as shown in the following formula:(19)C2=nF1∗F2F1∗F221/2,where F1, F2 represent various fusion factors and ∗ represents the fusion situation. This formula can measure the degree of integration between the cultural industry and the tourism industry in the integration link. It can be obtained from the formula: the larger the value of C2, the greater the degree of fusion.In order to clearly express the results of industry integration, according to the research of Gangmin et al., the scope and level of integration are divided, as shown in Table3.Table 3
Division of industrial integration evaluation index system.
DivisionFusion interval (D)Coordination levelUncoordinated0.000–0.099Extremely out of balance0.100–0.199Severely disordered0.200–0.299Moderately disordered0.300–0.399Mild disorder0.400–0.499On the verge of dysregulationCoordination0.500–0.599Barely coordinated0.600–0.699Initial coordination0.700–0.799Intermediate coordinator0.800–0.899Well coordinated0.900–0.999Excellent coordination


## 5.2.1. Entropy Method to Determine Weight

In order to study the problem of the integrated development of cultural tourism, this paper divides the integrated development of cultural tourism into a three-layer structure according to the structure of large indicators, medium indicators, and small indicators. However, the data of these indicators come from different departments and units, and their importance in the system is also different, which requires a method to quantify these different data. Traditional methods cannot accurately obtain the required conclusions. The entropy value method used in this paper is a method of index weight according to the size of the index, and its steps can be divided into 7 steps:(1)
Use the matrix of the following formula to construct a data matrix, wherexij represents the specific value of the small index j in the large index i.(1)A=x11⋯x1m⋮⋱⋮xn1⋯xnm.(2)
Use the ratio of a certain index to the sum of the same index to deal with the nonnegativity of the value.(3)
Use the following formula to calculate the weight coefficient of each small index in the large index.(2)pij=xij∑i=1nxij,j=1,2,….(4)
Calculate its entropy for the index of thejth item, as shown in the following formula:(3)ej=−K∗i=∑npij∗lnpij.HereK requires a nonnegative number and ej requires a value greater than or equal to zero. The constant K is inseparable from the sample size m; then it is set, as in the following formula:(4)K=1lnn.Then,0≤ej≤1.(5)
Calculate the difference coefficient of different indicators: for an index, the larger the difference coefficient, the greater the effect and the smaller the entropy value.(6)
Use the following formula to obtain the weight of each indicator:(5)Wj=Gj∑j=1mGj,j=1,2,…,m,whereGj is expressed as follows:(6)G=j1−ej.The larger the obtainedGj is, the more important the indicator is.(7)
Calculate the comprehensive score of each major indicator, as shown in the following formula:(7)Si=∑j=1mWj∗pij,i,j=1,2,….


## 5.2.2. Improved Entropy Method

(1)
Establish a matrix of initial data, as in the following formula:(8)X=xij.(2)
Standardize the data:The data in the indicator table comes from different departments, and the units used are also different, which must be adjusted. In this paper, formula9 and formula 10 are used to standardize the raw data of the indicators to achieve the purpose of quantification. The data to be processed must be a positive number and between 0 and 1 to serve the subsequent calculation. The specific operation is calculated according to the positive and negative classification. When the calculation is positive, use formula 9, and when the calculation is negative, use formula 10:(9)Vij=xij−xminxmax−xmin,(10)Vij=xmax−xijxmax−xmin.It is worth noting that scale normalization will bring a value of 0, which is not good for taking logarithms and calculating entropy values at a later stage. Therefore, using the method of minimum offset, setting all 0 values to 0.00001, which does not affect the final value, the entropy value can be calculated, and the number of points for comprehensive evaluation can be obtained.(3)
The entropy value is calculated as follows:(11)ej=−1lnm∑i=1mpijlnpij,wherepij is expressed as follows:(12)pij=Vij∑i=1mVij.Among them,Vij is the standardized data.(4)
Use the following formula to calculate the coefficient of difference of thejth index:(13)gj=1−ej.(5)
The weight calculation is as follows:(14)Wij=gij∑j=1ngij.(6)
Use the following formula to obtain the overall high-level development score for each subject:(15)Z=100×∑pij×wj.


## 5.2.3. Fusion Degree Model

(16)Fii=1∼m,(17)Fjj=1∼n.Formulas (16) and (17) represent the number of internal industries. When n is greater than 2, the mathematical calculation of the integration degree of interaction between all departments in the industry with the number n is as follows:(18)Cn=nF1∗F2∗…∗FnF1∗F2∗…∗Fnn1/n.Lettingn = 2, the fusion degree model becomes as shown in the following formula:(19)C2=nF1∗F2F1∗F221/2,where F1, F2 represent various fusion factors and ∗ represents the fusion situation. This formula can measure the degree of integration between the cultural industry and the tourism industry in the integration link. It can be obtained from the formula: the larger the value of C2, the greater the degree of fusion.In order to clearly express the results of industry integration, according to the research of Gangmin et al., the scope and level of integration are divided, as shown in Table3.Table 3
Division of industrial integration evaluation index system.
DivisionFusion interval (D)Coordination levelUncoordinated0.000–0.099Extremely out of balance0.100–0.199Severely disordered0.200–0.299Moderately disordered0.300–0.399Mild disorder0.400–0.499On the verge of dysregulationCoordination0.500–0.599Barely coordinated0.600–0.699Initial coordination0.700–0.799Intermediate coordinator0.800–0.899Well coordinated0.900–0.999Excellent coordination


## 5.3. Calculation and Analysis of Cultural and Tourism Industry Integration

The relevant data are obtained from the statistical public statements and economic development announcement forms from 2010 to 2018, as shown in Table4.Table 4
Statistical table of cultural industry and tourism industry indicators from 2010 to 2018.
201020112012201320142015201620172018179.7114.2141.1154.7152.1173.4213.9252.61130.824.95.35.25.566.38.78.98328128225329332133433534235346.56.55.35.13.73.13.54.4355121.827.516.68.2712.213.47.263373383383383383373953953957213123192222232323810911356164218521896157817.9.11997.82293.797084.610908.916921.127852.7935112127.212685.114379.918386.8102429363634343434351135863186358172536476584156984243652444420146398513524131211211087103511321221123114041175168213610.4805.3141212281663204225053035371614575.9777100111201618198824273034371415277624564161345736587403887266124197130665144670166780.99554112561534218427189071976020708239811780.5123.2158.9176.8198.2131.4273.6302.4341.9182592902963393654094685306311911211110810311711198988220313242394950677679With the help of the calculation formula of the entropy method, the data in Table4 are brought into the formula for calculation, and the index weights of the cultural industry and tourism industry from 2010 to 2018 are obtained. The weight of the indicator represents the importance of the indicator in the model. The larger the weight, the higher the importance of the indicator, as shown in Figure 9.Figure 9
Evaluation indicators of cultural and tourism industry integration.The entropy algorithm is used to obtain the value of the weight and put this value into the fusion model for in-depth calculation to obtain the overall evaluation level (i.e.,F1 and F2) and the degree of fusion (D) involved in the cultural and tourism industry from 2010 to 2018, as shown in Table 5 and Figure 10.Table 5
Level of integration of cultural and tourism industries from 2010 to 2018.
yearsType of developmentCoordination level2010Tourism lagsModerate coordination2011Tourism lagsLight coordination2012Tourism lagsLight coordination2013Tourism lagsOn the verge of dysregulation2014Cultural lagLight coordination2015Cultural lagLight coordination2016Tourism lagsBarely coordinated2017Cultural lagBarely coordinated2018Tourism lagsPrimary coordinationFigure 10
Trend chart of the integrated development level of cultural industry and tourism industry from 2010 to 2018.Conclusions can be drawn from Table5 and Figure 10. From the perspective of development trends, from 2010 to 2013, 2016, and 2018, the progress of tourism was lower than that of culture. From 2014 to 2015 and 2017, it can be seen that the overall cultural development level is far behind its tourism development level. According to the degree of integration (D), in the years from 2010 to 2018, both showed a gradual growth trend. According to the data from 2010 to 2017, it is obvious that the degree of fusion developed from moderate dissonance to reluctance, and in 2018, the two achieved a state of initial fusion. According to the results, the cultural tourism industry has made great progress in the process of integration, and the integration and development of these two industries is imperative.


## 6. Conclusions

For the cultural and tourism industries, the integration of the two should not only comprehensively coordinate the integration of all aspects of culture and tourism, but also consider market adjustments and the response to sudden crises. The integration of cultural tourism requires integration in all aspects of cultural and tourism industries. In this paper, the background and related research of this paper are first introduced. It is believed that most of the current research on cultural and tourism integration is relatively macro, and there is no rational analysis on the data package. After that, a brief introduction to the theories of culture, tourism industry, big data, and deep learning in this article is given. Afterwards, the big data platform was designed for the cultural tourism industry, and the reliability of the system was verified. And following up model fitting for the integration of the cultural tourism industry, it is believed that the current development of the cultural tourism industry is lagging behind, and the integration of the two is imperative. However, at the same time, there are also inaccuracies in this article. For example, in the design of the big data platform, the division of regions is not taken into account. Such results may not be too targeted. Therefore, this area will be studied in the follow-up research.



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*Source: 4116659-2022-05-17.xml*