# The Development Path and Data Mining Mode of Rural Tourism under the Background of Big Data

Authors: Liying Chen; Hong Hu; Xun He; Meng Lyu
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

With the advent of the era of mass tourism, rural tourism has received increasing attention from tourism-related personnel, such as the wide application of Internet, cloud computing, and big data technology, making it possible to build a development path for rural tourism based on the background of big data. The purpose of this paper is to study the development path and data mining mode analysis of rural tourism under the background of data and propose a clustering integration algorithm. The article gives an overview of the fit relationship between rural tourism and big data. This paper takes the farm tourism data in area A as the research object and through the data analysis of the rural tourism development path using the clustering integration algorithm; it explores the effectiveness of the clustering integration algorithm in the construction of the rural tourism development path. The experimental results show that compared with the traditional rural tourism development model in 2020, the tourism revenue in 2021 will increase by more than 300,000 yuan.


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## Body

## 1. Introduction

In recent years, with the development of social economy, people’s quality of life has also undergone earth-shaking changes, and the proportion of tourism consumption in people’s daily expenditure is also increasing. This has greatly increased the employment rate of the tourism industry, which plays a pivotal role in the country’s economic growth. The tourism market and tourism topics have also received increasing attention from various social personnel, and tourism employment, investment, and entrepreneurship have become increasingly popular. In recent years, the tourism industry has gradually entered people’s field of vision. With the increasing pressure of contemporary life, tourism has become one of the ways for people to release pressure and obtain the highest happiness index [1, 2]. Tourism has become life-oriented and universal, and people will usher in the era of mass tourism. With the growing role of tourism in the world economy, it has become an important part of poverty alleviation and sustainable development. Rural tourism, as its name implies, is a kind of tourism activity developed in rural areas with the support of agricultural industry. It mainly attracts tourists with beautiful rural environment and other rural resources. The development of rural tourism not only enables urban residents to experience different rural life but also promotes exchanges between urban and rural residents, enabling cities and villages to develop and progress together. The development of rural tourism not only makes people have new ideas about tourism resources but also promotes the rural economy and promotes the progress and construction of rural social civilization.With the continuous integration of high technology and tourism, tourism big data has also received one of the important concerns of people. Big data is a new technology evolved on the basis of information technologies such as the Internet and cloud computing. In recent years, it has been gradually applied to all aspects of society. The tourism route developed under the background of big data can provide tourists with more comprehensive services, so that tourists can experience various tourism projects more realistically. Therefore, the use of tourism big data plays an irreplaceable role in the experience of tourism services, the promotion of tourism markets, the development of tourism products, and the promotion of tourism development. Not only that, tourism big data has led an important direction for economic growth and has indescribable social value. This has certain significance for the promotion and sustainable development of the tourism field.In recent years, many experts and scholars have mainly focused on the research of big data technology. However, they did not pay much attention to the application of this aspect in tourism development. They only talked about the advantages of big data technology in tourism such as cities and villages from the side and lacked specific conceptual research and practical exploration. Therefore, this paper explores the development path and data mining mode analysis of rural tourism under the background of big data, so as to promote the development of big data technology and also provide reference for future related research on rural tourism.


## 2. Related Work

In order to improve the traditional method for evaluating the operation status of hydrogenation heat exchangers, Li et al. proposed a big data-based evaluation method for the operation of hydrogenation heat exchangers. It shows that the evaluation of the operation status of hydrofinishing heat exchange units based on big data technology will help operators to more accurately grasp the operation status of industrial systems and has positive guiding significance for fault early warning [3]. With the advent of the era of big data (EBD), the concept of customized tourism has gradually entered people’s lives. Zhang et al.. mainly introduce the research on the influence of e-commerce tourism on EBD customized tourism, aiming to provide some ideas and directions for the good development of customized tourism. He proposed a research strategy on the impact of tourism e-commerce on customized tourism in EBD for customized tourism e-commerce. The experimental results show that 79.84% of customers are willing to buy related products again after using big data technology to experience customized travel services [4]. Athey shows that machine learning prediction methods are highly effective in applications ranging from medicine to assigning city fire and sanitation inspectors. However, there are many gaps between making predictions and making decisions, and the underlying assumptions need to be understood to optimize data-driven decision making [5]. In order to reduce the amount of data collected by IoT, he increased the processing speed of big data. In order to reduce the data collected by the Internet of Things, he proposed the method of compressed sensing sampling. In view of the high computational complexity of the compressed sensing algorithm, Xue et al. use the multiobjective optimization particle swarm optimization algorithm to improve the search terms of the gradient projection sparse reconstruction algorithm (GPSR-BB). The results show that the proposed multiobjective particle swarm optimization-genetic algorithm (MOPSOGA) reduces the number of iterations by 51.6% compared to the traditional GPSR-BB algorithm, and it has better reconstruction performance [6]. Diversity and accuracy are two distinct characteristics of large-scale and heterogeneous data. It has always been a great challenge to represent and process big data efficiently with a unified scheme. Kuang et al. proposed a unified tensor model to represent unstructured, semistructured, and structured data. Studies have shown that the proposed unified tensor model is effective for big data representation and dimensionality reduction [7]. In recent years, an emerging research topic in data mining, known as privacy-preserving data mining (PPDM), has been extensively studied. The current research on PPDM mainly focuses on how to reduce the privacy risks brought by data mining operations. In fact, in the process of data collection, data release, and information transfer, accidental disclosure of sensitive information may also occur. Not only did Xu et al. identify four different types of users but are also involved in data mining applications, namely, data providers, data collectors, data miners, and decision-makers. And he discusses the privacy concerns of each type of user and the methods that can be used to protect sensitive information [8]. Advances in information technology have witnessed tremendous advancements in healthcare technology in various fields today. However, these new technologies have also made healthcare data not only larger, but more difficult to process. In order to provide a more convenient healthcare service and environment, Zhang and other scholars proposed a cyberphysical system for patient-centric healthcare applications and services based on cloud and big data analysis technology, called Health-CPS. The results show that cloud and big data technologies can be used to improve the performance of healthcare systems so that humans can enjoy various smart healthcare applications and services [9]. To sum up, big data technology has been applied to various fields, including industrial fields and medical industries. However, while all countries in the world are committed to the development of tourism, there are not many studies on the application of big data technology in the development of rural tourism; so, more in-depth exploration is needed.


## 3. Relevant Research Theories of Rural Tourism Development Path and Data Mining Mode Analysis under the Background of Big Data

### 3.1. Theories Related to Big Data

Big data uses the entire data sample to analyze and deal with problems, so that the analysis results are more accurate and reflect the actual situation. Because extracting some data can only reflect local results, there are many drawbacks. Big data analysis can accurately understand the market development situation, clarify the market positioning, and achieve the purpose of precise marketing [10, 11]. Therefore, big data can improve the decision-making power of enterprises and improve the efficiency of enterprise management. Big data gets the correlation between things through data collection and analysis. When dealing with a problem, it is not necessary to find the cause of the problem, as long as the person knows the statistics of such a problem, and it will know the corresponding results. Because the results of big data analysis often contain many aspects, the results of each aspect are not independent, but are correlated and can be mutually verified [12, 13].


### 3.2. The Fit Relationship between Big Data and Rural Tourism

With the advancement of social science and technology, agricultural production methods have been continuously improved. The continuous movement of the rural population to the cities led to a decline in the rural population and economic depression [14–16]. It is also under such circumstances that the government and scholars have begun to pay attention to the development of rural industries. Urban culture has gradually repulsed the development of tourism, which has ushered in an opportunity for the development of beautiful rural areas. More and more people like to travel in remote and attractive rural areas [17, 18]. The countryside has become a tourist destination for the public in recent years, which constitutes the early form of rural ecotourism, as shown in Figure 1.Figure 1
The formation of rural tourism.Rural tourism is guided by the concept of ecotourism, with the beautiful rural environment as the attraction, and a new type of ecotourism with folk customs and pastoral scenery as the main form of experience. Ecotourism is a kind of tourism activity with strong ecological protection awareness. It not only requires tourists to have natural protection awareness but also requires operators and local residents to have environmental protection awareness [19–21]. Rural ecotourism is a new form of tourism developed from the concepts of rural tourism and ecotourism. It is mainly through the protection of the environment to achieve the purpose of sustainable development of the local ecology and population. The relationship between these three is shown in Figure 2.Figure 2
Relationship between rural tourism, ecotourism, and rural ecotourism.The development of rural tourism will generate rural tourism big data, such as short video App data and TV tourism communication data. These data are generated to serve tourists. Therefore, these data are of great value. It can be collected, stored, analyzed, and used.The development of rural tourism is inseparable from big data, and data has gradually become the cornerstone of tourism development. Without big data, tourists will not be able to learn about tourism information, and tourism companies will not be able to better promote tourism resources. The application of big data can provide useful tourism data for tourists, tourism enterprises, and tourism management [22]. It not only enables tourists to experience more tourism projects, but also promotes the transformation of rural tourism from new to old, which greatly promotes the development of rural tourism industry.Tourism big data and rural tourism promote and integrate with each other. The use of tourism big data promotes the development of rural tourism, and the rural tourism industry promotes the generation of tourism big data. The application of big data is the general trend of transformation and upgrading in the field of rural tourism.


### 3.3. The Coordinated Development Path of Rural Tourism under the Background of Big Data

According to the relationship between rural tourism and big data, find the incongruous factors in the development and construction of the two and construct the coordinated development path of rural tourism under the background of big data. It mainly consists of five paths: environmental synergy, industrial synergy, cultural synergy, functional synergy, and management synergy, as shown in Figure3.Figure 3
Framework diagram of the coordinated development path of rural tourism and big data.Industrial synergy is the core of rural prosperity and the foundation of rural economic construction. The development of rural industries should give top priority to vigorously developing rural productivity and accelerating agricultural modernization and promoting the transformation and upgrading of rural industries according to the needs of the social market. Rural areas focus on “green ecology”, vigorously develop major enterprises, and accelerate the production area ecology, industrial integration, green products, and high-efficiency modern industrial patterns [23, 24]. At the same time, create a collective economic growth mode, actively encourage farmers to start their own businesses collectively, and work together to make the rural economy more prosperous. For the development of rural tourism in the context of big data, industrial support is inseparable. Therefore, make full use of the actual conditions and local characteristic resources, develop a diversified economy, increase production and income, and lay the foundation for sustainable development of rural construction.Environmental coordination is an important way to realize rural “ecological livability,” and it is also the focus of national ecological construction. To create rural tourism under the background of big data, it is necessary to deeply excavate rural culture, and it is not possible to form the same model of hundreds of villages and to build the countryside into a beautiful environment with original local characteristics and modern flavor. Rural construction should aim at “cleanliness, beauty, harmony and livability” and create rural homestays, beautiful farms, style courtyards, comfortable towns, etc. It allows farmers not only to enjoy natural and comfortable homes but also to attract tourists to experience different folk customs [25]. The rural tourism industry must follow nature, develop ecological agriculture and green industries, adhere to the harmonious coexistence of man and nature, and take the path of green rural development.The cultural synergy path includes three aspects: culture, civilization, and style. The countryside is the origin of traditional culture and an important carrier of farming civilization. Rural tourism under the background of big data needs to deeply explore rural stories and culture, protect historical and cultural villages, and combine modern scientific and technological means to protect the original features of the countryside, so that the local culture can be better inherited. In promoting the rural tourism industry, it is also very important to strengthen rural cultural education, promoting the construction of a series of rural projects such as rural bookstores, rural cultural stations, and rural communities. This not only enriches the leisure life of rural residents but also guides villagers to actively study, unite, and live in harmony. It makes the villagers pay attention to the construction of rural civilization not only in work but also in daily life [26, 27]. Villages should be developed according to local conditions to form their own unique rural style, excavating traditional farming culture, human settlement culture, landscape culture, attaching importance to the inheritance of culture, using all cultural positions to develop various ecological civilizations, and forming a new trend of rural civilization.Management coordination can achieve “effective governance” in rural areas and promote the harmonious development of rural areas. In order to meet the needs of villagers for a better and harmonious life, it is necessary to strengthen the basic work of rural agriculture and realize a sustainable rural management model, improve the rural management model and give play to the role of grassroots leaders, analyze the reasons for the real problems in rural areas and be brave in innovation, actively explore new models of rural governance, promote the openness of village affairs, give play to the roles of various social talents, rural residents, and other groups, and promote the construction of safe and harmonious rural areas. At the same time, grass-root leaders need to play an exemplary role in guiding villagers to self-govern themselves, build a scientific rural governance model with rural characteristics, and form an “effective governance” rural civilized order that is shared and shared.The improvement of the coordination of rural functions can realize the “rich life” in the rural areas, which is the expectation of rural residents. Rural hardware facilities should strengthen environmental awareness and tourism awareness, so that rural tourism and rural hardware facilities are at the same pace. In addition to the transformation of the rural landscape, the integration with tourism and tourism should also be achieved in terms of residential characteristics and agricultural industrialization. In addition, let the urban and rural integration develop to achieve the goal of common progress of urban and rural areas [28]. First, improve the construction of various perspectives in the countryside and form a new relationship of mutual benefit and urban-rural integration between rural residents. Second, promote the allocation of excellent resources to rural areas and promote the inflow of funds and talents to rural areas, for example, encouraging capital investment in rural areas and supporting farmers in reverse entrepreneurship.


### 3.4. Data Mining

Data mining is the process of searching and cleaning the data that people need by using big data technology, mainly data mining technology, from the data of large databases. The information extracted from the database by data mining technology is usually represented in the form of models, concepts, rules, etc. Data is useful information and has value. Because in the process of data processing and analysis, it occupies human labor time and contains the value of human labor [29, 30]. In addition, data can be traded and used, and data will also generate certain value after use. Data cleansing is the process in which data is reexamined and reconciled. The purpose is to remove duplicate information, correct existing errors, and provide data consistency and security. Tourism big data is to classify and save tourism data through the aggregation of various aspects of tourism data to achieve unified management and collection of data, as shown in Figure 4.Figure 4
Functional diagram of tourism big data center.There are five most common big data mining analysis algorithms, as shown in Table1.Table 1
Big data mining analysis algorithm.
Serial numberAppellationThe main purpose1Maximum expectation (EM) algorithmFind the most likely parameter estimates2Fast Fourier transformDigital signal processing3Branch and bound algorithmLooking for a specific optimal solution4Cluster searchOptimize the best first search algorithm5Clustering ensemble algorithmFind data clustersThis paper mainly introduces the clustering ensemble algorithm, which mainly uses a special form to divide all group sample data into small parts with the same number. Each of these small parts is called a class in the algorithmic clustering process. The most critical in the clustering algorithm is the distance measure function of group sample data and its similarity measure. Thei-th sample xi=xi1,xi2,⋯,xid, the j-th sample xj=xj1,xj2,⋯,xjd, and d of the set of n data samples are their attribute numbers. Generally, the distance Distxi,xj between samples is used to measure the similarity between samples. The larger the Distxi,xj, the smaller the similarity between samples i and j, and the smaller the Distxi,xj, the greater the similarity between samples i and j. Common methods for calculating Distxi,xj are roughly divided into the following categories:
(1)
Euclidean distance is as follows:(1)Distxi,xj=xi1−xj12+xi2−xj22+⋯+xid−xjd2.(2)
Manhattan distance is as follows:(2)Distxi,xj=xi1−xj1+xi2−xj2+⋯+xid−xjd.(3)
Minkowski distance is as follows:(3)Distxi,xj=xi1−xj1h+xi2−xj2h+⋯+xid−xjdh1/h.(4)
Cosine distance of included angle is as follows:(4)Distxi,xj=1−simxi,xj=1−∑k=1dxik∙xjk∑k=1dxik2∙∑k=1dxjk2.In the current field of big data mining research, clustering ensemble algorithms have attracted the attention and focus of a large number of scholars [31, 32]. When researching this algorithm, most of the research focuses on the consensus function of the algorithm. On the premise of not affecting the inherent characteristics of sample data, an effective formula function is designed to maximize the clustering value of sample data. Then, the cluster components that have been passed in are combined together through the mapping function to form a new sample collection and passed out again. The general design method is shown in Figure 5:Figure 5
Common design methods of consensus functions


#### 3.4.1. Commonly Connected Matrix Method

The coconnection matrix method characterizes all cluster components with a matrix of sizen×n (n is the number of samples in the dataset). The frequency of each component in the n×n matrix belongs to the same cluster, which is also equivalent to the similarity measure matrix of the original sample group data. Each part inside represents the similarity strength of each sample data, and the matrix expression formula is shown in the formula:
(5)Co−associationxi,xj=1H∑h=1Hδπhxi,πhxj,(6)δx,y=0otherwise1ifx=y.Among them,Co−associationxi,xj is expressed as the element value corresponding to row i and column j of the coconnection matrix, that is, the ratio of the number of times that the i-th sample and the j-th sample are divided together to the total number of cluster members.


#### 3.4.2. Mutual Information Law

Mutual information method mutual information is known as a measure of the degree of correlation between different two groups of events in the methods of probability theory and mathematical statistics. In the clustering integration algorithm, mutual information can also be defined as a parameter that is used to detect the specific information of data under different distributions and is symmetrical to each other. Assuming thatπa and πb represent two cluster members, the normalized mutual information of these two cluster members is defined as
(7)NMIπa,πb=∑h=1ka∑l=1kbnh,llogn∙nh,l/nhanlb∑h=1kanhalognha/n∑l=1kbnlblognlb/n.The meaning of each parameter of Formula (7) is shown in Table 2.Table 2
The meaning of formula parameters.
SequenceParameterMeaning1kaNumber of clusters ofπa2kbNumber of clusters ofπb3nhaThe number of samples belonging to classCha in cluster member πa4nlbThe number of samples belonging to classClb in cluster member πb5nh,lThe number of samples belonging to classCha in cluster member πa and class Clb in cluster member πb at the same time6NMIπa,πbThe degree of shared information in the two clustering resultsThe goal of consensus function design based on mutual information method is to find a clustering partition, and the mutual information between this clustering partition and all cluster members is the largest. The definition of the target clustering can be expressed as(8)πopt=argmaxπ∑h=1HNMIπ,πh.


#### 3.4.3. Hypergraph Method

The clustering integration algorithms based on hypergraph partitioning are mainly divided intoCSPA, HGPA, and MCLA. The similarity matrix CSPA is defined as
(9)S=1hHHT.In the topology of the self-organizing feature, it is a neural network model commonly used in cluster analysis, mapping network is shown in Figure6.Figure 6
Self-organizing feature map network topology.In the process of forming the self-organizing map, the SOM algorithm needs to calculate the distance between the current input vector and the neurons in the competition layer and select the neuron with the closest distance as the winning neuronqt:
(10)qt=argminjxkt−wjt.Adjust the weight vector of the winning neuron and the neurons in its neighborhood as(11)wjt+1=wjt+ηtxkt−wjtj∈Nqtwjtj∈Nqt.η is the learning rate parameter, and 0<η<1 and Nqt are the current topological neighborhood radii of the winning neuron q, both of which are decreasing functions with increasing EE over time.The common functional forms of learning rateη are as follows:
(1)
Linear function is as follows:(12)ηt=η01−tT.(2)
Inverse proportional function is as follows:(13)ηt=η0t.(3)
Power function is as follows:(14)ηt=η0e−1/T.Among them,η0 generally takes a larger positive number less than 1, and T is the total number of iterations of the algorithm.The size of the topological neighborhood radiusNqt also shrinks with the increase of time t, and its general functional form is as follows:
(15)Nqt=Nq0e−t/t1.Among them, the initial field radius whereNq0 is generally the network width, t1 is the time constant, which generally takes
(16)t1=TlogNq0.Clustering comprehensive qualityOcq is a relative clustering evaluation method that combines clustering density and clustering separation. The Ocq index is defined as
(17)Ocqβ=1−β∙Cmp+1−β∙Sep.Cmp stands for cluster density, Sep stands for cluster separation, and β stands for balance coefficient.For a given datasetX=x1,x2,⋯,xN, it defines its within-cluster variance as
(18)Devx=1N∑i=1Nd2xi,x¯,where N is the number of samples in dataset X, dxi,x¯ represents the distance between xi and x¯, and x¯ is the mean X of the dataset, namely,
(19)x¯=1N∑i=1Nxi.The smaller the intracluster varianceDevx, the denser the overall internal distribution structure of the dataset, and the higher the sample identity.If the clustering result divides the dataset intoC=c1,c2,⋯,cc, the cluster density is defined as
(20)Cmp=1C∑i=1CDevciDevX,where C is the number of clusters, and Devci is the variance of class ci. According to the principle that the clustering results should ensure the maximum similarity within the class, in order to make the data within the cluster as compact as possible, the smaller the clustering density, the better [33].The cluster separation is defined as(21)Sep=1CC−1∑i=1C∑j=1,j≠1Cexp−d2xci,xcj2δ2,where δ is a Gaussian constant, for the convenience of calculation, 2δ2=1, and xci and xcj are usually set as the cluster center of the class ci and the class. According to the principle that the clustering results should ensure the minimum similarity between classes cj, in order to make the data between clusters as separate as possible, the smaller the cluster separation, the better. From the definition of Cmp and Sep, it can be seen that the larger the comprehensive quality C value of clustering, the better the clustering effect [34].In order to further verify the performance of the clustering ensemble algorithm, this paper obtains the clustering accuracy and test accuracy of the algorithm through numerical experiments of the algorithm. The experimental data are from the UCI dataset. The experimental results of the numerical clustering accuracy of the clustering ensemble algorithm are shown in Table3. The experimental results of the numerical accuracy of the algorithm are shown in Table 4. The experimental results show that the algorithm has good performance and clustering ability.Table 3
Numerical clustering accuracy experiment results of clustering ensemble algorithm.
DatasetNumber of samples in the datasetNumber of dataset categoriesAttributes contained in each dataset sample pointAlgorithm clustering accuracyGlass21469 dimensions86.93%Boston17891013 dimensions89.52%Housing20360817 dimensions91.60%Table 4
The experimental results of the numerical accuracy of the clustering ensemble algorithm.
DatasetParameterCCPU time (s)Test accuracyGlass100.3693.25%Boston110.5385.29%Housing60.4187.93%This clustering algorithm is used to construct the development path of rural tourism, use various characteristics of tourism big data to conduct data mining and classification, and provide users with personalized tourism search services. At the same time, big data technology can provide basic technical support for rural tourism development.


## 3.1. Theories Related to Big Data

Big data uses the entire data sample to analyze and deal with problems, so that the analysis results are more accurate and reflect the actual situation. Because extracting some data can only reflect local results, there are many drawbacks. Big data analysis can accurately understand the market development situation, clarify the market positioning, and achieve the purpose of precise marketing [10, 11]. Therefore, big data can improve the decision-making power of enterprises and improve the efficiency of enterprise management. Big data gets the correlation between things through data collection and analysis. When dealing with a problem, it is not necessary to find the cause of the problem, as long as the person knows the statistics of such a problem, and it will know the corresponding results. Because the results of big data analysis often contain many aspects, the results of each aspect are not independent, but are correlated and can be mutually verified [12, 13].


## 3.2. The Fit Relationship between Big Data and Rural Tourism

With the advancement of social science and technology, agricultural production methods have been continuously improved. The continuous movement of the rural population to the cities led to a decline in the rural population and economic depression [14–16]. It is also under such circumstances that the government and scholars have begun to pay attention to the development of rural industries. Urban culture has gradually repulsed the development of tourism, which has ushered in an opportunity for the development of beautiful rural areas. More and more people like to travel in remote and attractive rural areas [17, 18]. The countryside has become a tourist destination for the public in recent years, which constitutes the early form of rural ecotourism, as shown in Figure 1.Figure 1
The formation of rural tourism.Rural tourism is guided by the concept of ecotourism, with the beautiful rural environment as the attraction, and a new type of ecotourism with folk customs and pastoral scenery as the main form of experience. Ecotourism is a kind of tourism activity with strong ecological protection awareness. It not only requires tourists to have natural protection awareness but also requires operators and local residents to have environmental protection awareness [19–21]. Rural ecotourism is a new form of tourism developed from the concepts of rural tourism and ecotourism. It is mainly through the protection of the environment to achieve the purpose of sustainable development of the local ecology and population. The relationship between these three is shown in Figure 2.Figure 2
Relationship between rural tourism, ecotourism, and rural ecotourism.The development of rural tourism will generate rural tourism big data, such as short video App data and TV tourism communication data. These data are generated to serve tourists. Therefore, these data are of great value. It can be collected, stored, analyzed, and used.The development of rural tourism is inseparable from big data, and data has gradually become the cornerstone of tourism development. Without big data, tourists will not be able to learn about tourism information, and tourism companies will not be able to better promote tourism resources. The application of big data can provide useful tourism data for tourists, tourism enterprises, and tourism management [22]. It not only enables tourists to experience more tourism projects, but also promotes the transformation of rural tourism from new to old, which greatly promotes the development of rural tourism industry.Tourism big data and rural tourism promote and integrate with each other. The use of tourism big data promotes the development of rural tourism, and the rural tourism industry promotes the generation of tourism big data. The application of big data is the general trend of transformation and upgrading in the field of rural tourism.


## 3.3. The Coordinated Development Path of Rural Tourism under the Background of Big Data

According to the relationship between rural tourism and big data, find the incongruous factors in the development and construction of the two and construct the coordinated development path of rural tourism under the background of big data. It mainly consists of five paths: environmental synergy, industrial synergy, cultural synergy, functional synergy, and management synergy, as shown in Figure3.Figure 3
Framework diagram of the coordinated development path of rural tourism and big data.Industrial synergy is the core of rural prosperity and the foundation of rural economic construction. The development of rural industries should give top priority to vigorously developing rural productivity and accelerating agricultural modernization and promoting the transformation and upgrading of rural industries according to the needs of the social market. Rural areas focus on “green ecology”, vigorously develop major enterprises, and accelerate the production area ecology, industrial integration, green products, and high-efficiency modern industrial patterns [23, 24]. At the same time, create a collective economic growth mode, actively encourage farmers to start their own businesses collectively, and work together to make the rural economy more prosperous. For the development of rural tourism in the context of big data, industrial support is inseparable. Therefore, make full use of the actual conditions and local characteristic resources, develop a diversified economy, increase production and income, and lay the foundation for sustainable development of rural construction.Environmental coordination is an important way to realize rural “ecological livability,” and it is also the focus of national ecological construction. To create rural tourism under the background of big data, it is necessary to deeply excavate rural culture, and it is not possible to form the same model of hundreds of villages and to build the countryside into a beautiful environment with original local characteristics and modern flavor. Rural construction should aim at “cleanliness, beauty, harmony and livability” and create rural homestays, beautiful farms, style courtyards, comfortable towns, etc. It allows farmers not only to enjoy natural and comfortable homes but also to attract tourists to experience different folk customs [25]. The rural tourism industry must follow nature, develop ecological agriculture and green industries, adhere to the harmonious coexistence of man and nature, and take the path of green rural development.The cultural synergy path includes three aspects: culture, civilization, and style. The countryside is the origin of traditional culture and an important carrier of farming civilization. Rural tourism under the background of big data needs to deeply explore rural stories and culture, protect historical and cultural villages, and combine modern scientific and technological means to protect the original features of the countryside, so that the local culture can be better inherited. In promoting the rural tourism industry, it is also very important to strengthen rural cultural education, promoting the construction of a series of rural projects such as rural bookstores, rural cultural stations, and rural communities. This not only enriches the leisure life of rural residents but also guides villagers to actively study, unite, and live in harmony. It makes the villagers pay attention to the construction of rural civilization not only in work but also in daily life [26, 27]. Villages should be developed according to local conditions to form their own unique rural style, excavating traditional farming culture, human settlement culture, landscape culture, attaching importance to the inheritance of culture, using all cultural positions to develop various ecological civilizations, and forming a new trend of rural civilization.Management coordination can achieve “effective governance” in rural areas and promote the harmonious development of rural areas. In order to meet the needs of villagers for a better and harmonious life, it is necessary to strengthen the basic work of rural agriculture and realize a sustainable rural management model, improve the rural management model and give play to the role of grassroots leaders, analyze the reasons for the real problems in rural areas and be brave in innovation, actively explore new models of rural governance, promote the openness of village affairs, give play to the roles of various social talents, rural residents, and other groups, and promote the construction of safe and harmonious rural areas. At the same time, grass-root leaders need to play an exemplary role in guiding villagers to self-govern themselves, build a scientific rural governance model with rural characteristics, and form an “effective governance” rural civilized order that is shared and shared.The improvement of the coordination of rural functions can realize the “rich life” in the rural areas, which is the expectation of rural residents. Rural hardware facilities should strengthen environmental awareness and tourism awareness, so that rural tourism and rural hardware facilities are at the same pace. In addition to the transformation of the rural landscape, the integration with tourism and tourism should also be achieved in terms of residential characteristics and agricultural industrialization. In addition, let the urban and rural integration develop to achieve the goal of common progress of urban and rural areas [28]. First, improve the construction of various perspectives in the countryside and form a new relationship of mutual benefit and urban-rural integration between rural residents. Second, promote the allocation of excellent resources to rural areas and promote the inflow of funds and talents to rural areas, for example, encouraging capital investment in rural areas and supporting farmers in reverse entrepreneurship.


## 3.4. Data Mining

Data mining is the process of searching and cleaning the data that people need by using big data technology, mainly data mining technology, from the data of large databases. The information extracted from the database by data mining technology is usually represented in the form of models, concepts, rules, etc. Data is useful information and has value. Because in the process of data processing and analysis, it occupies human labor time and contains the value of human labor [29, 30]. In addition, data can be traded and used, and data will also generate certain value after use. Data cleansing is the process in which data is reexamined and reconciled. The purpose is to remove duplicate information, correct existing errors, and provide data consistency and security. Tourism big data is to classify and save tourism data through the aggregation of various aspects of tourism data to achieve unified management and collection of data, as shown in Figure 4.Figure 4
Functional diagram of tourism big data center.There are five most common big data mining analysis algorithms, as shown in Table1.Table 1
Big data mining analysis algorithm.
Serial numberAppellationThe main purpose1Maximum expectation (EM) algorithmFind the most likely parameter estimates2Fast Fourier transformDigital signal processing3Branch and bound algorithmLooking for a specific optimal solution4Cluster searchOptimize the best first search algorithm5Clustering ensemble algorithmFind data clustersThis paper mainly introduces the clustering ensemble algorithm, which mainly uses a special form to divide all group sample data into small parts with the same number. Each of these small parts is called a class in the algorithmic clustering process. The most critical in the clustering algorithm is the distance measure function of group sample data and its similarity measure. Thei-th sample xi=xi1,xi2,⋯,xid, the j-th sample xj=xj1,xj2,⋯,xjd, and d of the set of n data samples are their attribute numbers. Generally, the distance Distxi,xj between samples is used to measure the similarity between samples. The larger the Distxi,xj, the smaller the similarity between samples i and j, and the smaller the Distxi,xj, the greater the similarity between samples i and j. Common methods for calculating Distxi,xj are roughly divided into the following categories:
(1)
Euclidean distance is as follows:(1)Distxi,xj=xi1−xj12+xi2−xj22+⋯+xid−xjd2.(2)
Manhattan distance is as follows:(2)Distxi,xj=xi1−xj1+xi2−xj2+⋯+xid−xjd.(3)
Minkowski distance is as follows:(3)Distxi,xj=xi1−xj1h+xi2−xj2h+⋯+xid−xjdh1/h.(4)
Cosine distance of included angle is as follows:(4)Distxi,xj=1−simxi,xj=1−∑k=1dxik∙xjk∑k=1dxik2∙∑k=1dxjk2.In the current field of big data mining research, clustering ensemble algorithms have attracted the attention and focus of a large number of scholars [31, 32]. When researching this algorithm, most of the research focuses on the consensus function of the algorithm. On the premise of not affecting the inherent characteristics of sample data, an effective formula function is designed to maximize the clustering value of sample data. Then, the cluster components that have been passed in are combined together through the mapping function to form a new sample collection and passed out again. The general design method is shown in Figure 5:Figure 5
Common design methods of consensus functions


### 3.4.1. Commonly Connected Matrix Method

The coconnection matrix method characterizes all cluster components with a matrix of sizen×n (n is the number of samples in the dataset). The frequency of each component in the n×n matrix belongs to the same cluster, which is also equivalent to the similarity measure matrix of the original sample group data. Each part inside represents the similarity strength of each sample data, and the matrix expression formula is shown in the formula:
(5)Co−associationxi,xj=1H∑h=1Hδπhxi,πhxj,(6)δx,y=0otherwise1ifx=y.Among them,Co−associationxi,xj is expressed as the element value corresponding to row i and column j of the coconnection matrix, that is, the ratio of the number of times that the i-th sample and the j-th sample are divided together to the total number of cluster members.


### 3.4.2. Mutual Information Law

Mutual information method mutual information is known as a measure of the degree of correlation between different two groups of events in the methods of probability theory and mathematical statistics. In the clustering integration algorithm, mutual information can also be defined as a parameter that is used to detect the specific information of data under different distributions and is symmetrical to each other. Assuming thatπa and πb represent two cluster members, the normalized mutual information of these two cluster members is defined as
(7)NMIπa,πb=∑h=1ka∑l=1kbnh,llogn∙nh,l/nhanlb∑h=1kanhalognha/n∑l=1kbnlblognlb/n.The meaning of each parameter of Formula (7) is shown in Table 2.Table 2
The meaning of formula parameters.
SequenceParameterMeaning1kaNumber of clusters ofπa2kbNumber of clusters ofπb3nhaThe number of samples belonging to classCha in cluster member πa4nlbThe number of samples belonging to classClb in cluster member πb5nh,lThe number of samples belonging to classCha in cluster member πa and class Clb in cluster member πb at the same time6NMIπa,πbThe degree of shared information in the two clustering resultsThe goal of consensus function design based on mutual information method is to find a clustering partition, and the mutual information between this clustering partition and all cluster members is the largest. The definition of the target clustering can be expressed as(8)πopt=argmaxπ∑h=1HNMIπ,πh.


### 3.4.3. Hypergraph Method

The clustering integration algorithms based on hypergraph partitioning are mainly divided intoCSPA, HGPA, and MCLA. The similarity matrix CSPA is defined as
(9)S=1hHHT.In the topology of the self-organizing feature, it is a neural network model commonly used in cluster analysis, mapping network is shown in Figure6.Figure 6
Self-organizing feature map network topology.In the process of forming the self-organizing map, the SOM algorithm needs to calculate the distance between the current input vector and the neurons in the competition layer and select the neuron with the closest distance as the winning neuronqt:
(10)qt=argminjxkt−wjt.Adjust the weight vector of the winning neuron and the neurons in its neighborhood as(11)wjt+1=wjt+ηtxkt−wjtj∈Nqtwjtj∈Nqt.η is the learning rate parameter, and 0<η<1 and Nqt are the current topological neighborhood radii of the winning neuron q, both of which are decreasing functions with increasing EE over time.The common functional forms of learning rateη are as follows:
(1)
Linear function is as follows:(12)ηt=η01−tT.(2)
Inverse proportional function is as follows:(13)ηt=η0t.(3)
Power function is as follows:(14)ηt=η0e−1/T.Among them,η0 generally takes a larger positive number less than 1, and T is the total number of iterations of the algorithm.The size of the topological neighborhood radiusNqt also shrinks with the increase of time t, and its general functional form is as follows:
(15)Nqt=Nq0e−t/t1.Among them, the initial field radius whereNq0 is generally the network width, t1 is the time constant, which generally takes
(16)t1=TlogNq0.Clustering comprehensive qualityOcq is a relative clustering evaluation method that combines clustering density and clustering separation. The Ocq index is defined as
(17)Ocqβ=1−β∙Cmp+1−β∙Sep.Cmp stands for cluster density, Sep stands for cluster separation, and β stands for balance coefficient.For a given datasetX=x1,x2,⋯,xN, it defines its within-cluster variance as
(18)Devx=1N∑i=1Nd2xi,x¯,where N is the number of samples in dataset X, dxi,x¯ represents the distance between xi and x¯, and x¯ is the mean X of the dataset, namely,
(19)x¯=1N∑i=1Nxi.The smaller the intracluster varianceDevx, the denser the overall internal distribution structure of the dataset, and the higher the sample identity.If the clustering result divides the dataset intoC=c1,c2,⋯,cc, the cluster density is defined as
(20)Cmp=1C∑i=1CDevciDevX,where C is the number of clusters, and Devci is the variance of class ci. According to the principle that the clustering results should ensure the maximum similarity within the class, in order to make the data within the cluster as compact as possible, the smaller the clustering density, the better [33].The cluster separation is defined as(21)Sep=1CC−1∑i=1C∑j=1,j≠1Cexp−d2xci,xcj2δ2,where δ is a Gaussian constant, for the convenience of calculation, 2δ2=1, and xci and xcj are usually set as the cluster center of the class ci and the class. According to the principle that the clustering results should ensure the minimum similarity between classes cj, in order to make the data between clusters as separate as possible, the smaller the cluster separation, the better. From the definition of Cmp and Sep, it can be seen that the larger the comprehensive quality C value of clustering, the better the clustering effect [34].In order to further verify the performance of the clustering ensemble algorithm, this paper obtains the clustering accuracy and test accuracy of the algorithm through numerical experiments of the algorithm. The experimental data are from the UCI dataset. The experimental results of the numerical clustering accuracy of the clustering ensemble algorithm are shown in Table3. The experimental results of the numerical accuracy of the algorithm are shown in Table 4. The experimental results show that the algorithm has good performance and clustering ability.Table 3
Numerical clustering accuracy experiment results of clustering ensemble algorithm.
DatasetNumber of samples in the datasetNumber of dataset categoriesAttributes contained in each dataset sample pointAlgorithm clustering accuracyGlass21469 dimensions86.93%Boston17891013 dimensions89.52%Housing20360817 dimensions91.60%Table 4
The experimental results of the numerical accuracy of the clustering ensemble algorithm.
DatasetParameterCCPU time (s)Test accuracyGlass100.3693.25%Boston110.5385.29%Housing60.4187.93%This clustering algorithm is used to construct the development path of rural tourism, use various characteristics of tourism big data to conduct data mining and classification, and provide users with personalized tourism search services. At the same time, big data technology can provide basic technical support for rural tourism development.


## 3.4.1. Commonly Connected Matrix Method

The coconnection matrix method characterizes all cluster components with a matrix of sizen×n (n is the number of samples in the dataset). The frequency of each component in the n×n matrix belongs to the same cluster, which is also equivalent to the similarity measure matrix of the original sample group data. Each part inside represents the similarity strength of each sample data, and the matrix expression formula is shown in the formula:
(5)Co−associationxi,xj=1H∑h=1Hδπhxi,πhxj,(6)δx,y=0otherwise1ifx=y.Among them,Co−associationxi,xj is expressed as the element value corresponding to row i and column j of the coconnection matrix, that is, the ratio of the number of times that the i-th sample and the j-th sample are divided together to the total number of cluster members.


## 3.4.2. Mutual Information Law

Mutual information method mutual information is known as a measure of the degree of correlation between different two groups of events in the methods of probability theory and mathematical statistics. In the clustering integration algorithm, mutual information can also be defined as a parameter that is used to detect the specific information of data under different distributions and is symmetrical to each other. Assuming thatπa and πb represent two cluster members, the normalized mutual information of these two cluster members is defined as
(7)NMIπa,πb=∑h=1ka∑l=1kbnh,llogn∙nh,l/nhanlb∑h=1kanhalognha/n∑l=1kbnlblognlb/n.The meaning of each parameter of Formula (7) is shown in Table 2.Table 2
The meaning of formula parameters.
SequenceParameterMeaning1kaNumber of clusters ofπa2kbNumber of clusters ofπb3nhaThe number of samples belonging to classCha in cluster member πa4nlbThe number of samples belonging to classClb in cluster member πb5nh,lThe number of samples belonging to classCha in cluster member πa and class Clb in cluster member πb at the same time6NMIπa,πbThe degree of shared information in the two clustering resultsThe goal of consensus function design based on mutual information method is to find a clustering partition, and the mutual information between this clustering partition and all cluster members is the largest. The definition of the target clustering can be expressed as(8)πopt=argmaxπ∑h=1HNMIπ,πh.


## 3.4.3. Hypergraph Method

The clustering integration algorithms based on hypergraph partitioning are mainly divided intoCSPA, HGPA, and MCLA. The similarity matrix CSPA is defined as
(9)S=1hHHT.In the topology of the self-organizing feature, it is a neural network model commonly used in cluster analysis, mapping network is shown in Figure6.Figure 6
Self-organizing feature map network topology.In the process of forming the self-organizing map, the SOM algorithm needs to calculate the distance between the current input vector and the neurons in the competition layer and select the neuron with the closest distance as the winning neuronqt:
(10)qt=argminjxkt−wjt.Adjust the weight vector of the winning neuron and the neurons in its neighborhood as(11)wjt+1=wjt+ηtxkt−wjtj∈Nqtwjtj∈Nqt.η is the learning rate parameter, and 0<η<1 and Nqt are the current topological neighborhood radii of the winning neuron q, both of which are decreasing functions with increasing EE over time.The common functional forms of learning rateη are as follows:
(1)
Linear function is as follows:(12)ηt=η01−tT.(2)
Inverse proportional function is as follows:(13)ηt=η0t.(3)
Power function is as follows:(14)ηt=η0e−1/T.Among them,η0 generally takes a larger positive number less than 1, and T is the total number of iterations of the algorithm.The size of the topological neighborhood radiusNqt also shrinks with the increase of time t, and its general functional form is as follows:
(15)Nqt=Nq0e−t/t1.Among them, the initial field radius whereNq0 is generally the network width, t1 is the time constant, which generally takes
(16)t1=TlogNq0.Clustering comprehensive qualityOcq is a relative clustering evaluation method that combines clustering density and clustering separation. The Ocq index is defined as
(17)Ocqβ=1−β∙Cmp+1−β∙Sep.Cmp stands for cluster density, Sep stands for cluster separation, and β stands for balance coefficient.For a given datasetX=x1,x2,⋯,xN, it defines its within-cluster variance as
(18)Devx=1N∑i=1Nd2xi,x¯,where N is the number of samples in dataset X, dxi,x¯ represents the distance between xi and x¯, and x¯ is the mean X of the dataset, namely,
(19)x¯=1N∑i=1Nxi.The smaller the intracluster varianceDevx, the denser the overall internal distribution structure of the dataset, and the higher the sample identity.If the clustering result divides the dataset intoC=c1,c2,⋯,cc, the cluster density is defined as
(20)Cmp=1C∑i=1CDevciDevX,where C is the number of clusters, and Devci is the variance of class ci. According to the principle that the clustering results should ensure the maximum similarity within the class, in order to make the data within the cluster as compact as possible, the smaller the clustering density, the better [33].The cluster separation is defined as(21)Sep=1CC−1∑i=1C∑j=1,j≠1Cexp−d2xci,xcj2δ2,where δ is a Gaussian constant, for the convenience of calculation, 2δ2=1, and xci and xcj are usually set as the cluster center of the class ci and the class. According to the principle that the clustering results should ensure the minimum similarity between classes cj, in order to make the data between clusters as separate as possible, the smaller the cluster separation, the better. From the definition of Cmp and Sep, it can be seen that the larger the comprehensive quality C value of clustering, the better the clustering effect [34].In order to further verify the performance of the clustering ensemble algorithm, this paper obtains the clustering accuracy and test accuracy of the algorithm through numerical experiments of the algorithm. The experimental data are from the UCI dataset. The experimental results of the numerical clustering accuracy of the clustering ensemble algorithm are shown in Table3. The experimental results of the numerical accuracy of the algorithm are shown in Table 4. The experimental results show that the algorithm has good performance and clustering ability.Table 3
Numerical clustering accuracy experiment results of clustering ensemble algorithm.
DatasetNumber of samples in the datasetNumber of dataset categoriesAttributes contained in each dataset sample pointAlgorithm clustering accuracyGlass21469 dimensions86.93%Boston17891013 dimensions89.52%Housing20360817 dimensions91.60%Table 4
The experimental results of the numerical accuracy of the clustering ensemble algorithm.
DatasetParameterCCPU time (s)Test accuracyGlass100.3693.25%Boston110.5385.29%Housing60.4187.93%This clustering algorithm is used to construct the development path of rural tourism, use various characteristics of tourism big data to conduct data mining and classification, and provide users with personalized tourism search services. At the same time, big data technology can provide basic technical support for rural tourism development.


## 4. Experimental Analysis of Clustering Algorithm Used in the Construction of Rural Tourism Development Path

This chapter will apply the clustering integration algorithm in the field of big data mining to study the problem of rural tourism development path construction. In this experiment, two areas with similar natural conditions and little difference in the number of farms were selected as the analysis objects. This paper adopts the traditional rural tourism development model (2020) and the rural tourism development model using clustering algorithm (2021) for comparative analysis. The first is the statistical analysis of farm tourism income in area A; the second is the distribution index of scenic spots in area A; the third is the statistical analysis of the per capita spending of farm tourists in area A; the fourth is the statistical analysis of the number of farm tourists in area A.


### 4.1. Statistical Analysis of Farm Tourism Income

As shown in Figure7, compared with the tourism income of farms in region A in 2020, through the rural tourism development model using the clustering algorithm, the tourism income of farms in region A will increase significantly in 2021. When the peak tourist season comes in October, the tourism revenue in 2020 will be 763,000 yuan, and the tourism revenue in 2021 will reach 1.067 million yuan, which is 304,000 yuan higher than that in 2020.Figure 7
Statistical analysis chart of farm tourism income in area A. (a) Statistical analysis chart of farmhouse tourism income in region A in 2020. (b) Statistical analysis chart of farmhouse tourism income in region A in 2021.
(a)(b)


### 4.2. Scenic Spot Distribution Index

It is not difficult to see from Figure8 that the number of scenic spots established in area A in 2020 will grow slowly, and the number of scenic spots throughout the year is as small as 9. In 2021, the number of scenic spots in area A will increase to 27 by December.Figure 8
Distribution index of scenic spots in farms and scenic spots in area A. (a) Distribution index of scenic spots in farms and scenic spots in region A in 2020. (b) The distribution index of scenic spots in farms and scenic spots in region A in 2021.
(a)(b)


### 4.3. Statistical Analysis of per Capita Spending of Tourists

Compared with the offseason period from November to January of the following year, the per capita spending of tourists on farms in region A will increase during the peak season from May to October. It can be seen from Figure9 that the clustering integration algorithm is used to construct the rural tourism development path, which can improve the farm economy in the A region. During the peak season, in 2020, the per capita spending of farmhouse tourists in region A will be at least 636 yuan, while in 2021, it will be at least 735 yuan. This shows that the clustering algorithm is used to construct the feasibility of rural tourism development path.Figure 9
Statistical analysis chart of per capita spending of farm tourists in area A. (a) Statistical analysis chart of per capita spending of tourists on farms in region A in 2020. (b) Statistical analysis chart of per capita spending of tourists on farms in region A in 2021.
(a)(b)


### 4.4. Statistical Analysis of the Number of Tourists in the Farm

According to the data results in Figure10, compared with 2020, the number of tourists to farms in region A increased from January to December. In October, the peak season, the number of tourists in 2020 and 2021 was 12.35 million and 15.36 million, respectively. The number of tourists in 2021 is 3.01 million more than in 2020. This shows that the clustering integration algorithm is effective in constructing the development path of rural tourism.Figure 10
Statistical analysis chart of the number of farm visitors in area A. (a) Statistical analysis chart of the number of tourists to farms in region A in 2020; (b) Statistical analysis of the number of tourists to farms in region A in 2021.
(a)(b)


## 4.1. Statistical Analysis of Farm Tourism Income

As shown in Figure7, compared with the tourism income of farms in region A in 2020, through the rural tourism development model using the clustering algorithm, the tourism income of farms in region A will increase significantly in 2021. When the peak tourist season comes in October, the tourism revenue in 2020 will be 763,000 yuan, and the tourism revenue in 2021 will reach 1.067 million yuan, which is 304,000 yuan higher than that in 2020.Figure 7
Statistical analysis chart of farm tourism income in area A. (a) Statistical analysis chart of farmhouse tourism income in region A in 2020. (b) Statistical analysis chart of farmhouse tourism income in region A in 2021.
(a)(b)


## 4.2. Scenic Spot Distribution Index

It is not difficult to see from Figure8 that the number of scenic spots established in area A in 2020 will grow slowly, and the number of scenic spots throughout the year is as small as 9. In 2021, the number of scenic spots in area A will increase to 27 by December.Figure 8
Distribution index of scenic spots in farms and scenic spots in area A. (a) Distribution index of scenic spots in farms and scenic spots in region A in 2020. (b) The distribution index of scenic spots in farms and scenic spots in region A in 2021.
(a)(b)


## 4.3. Statistical Analysis of per Capita Spending of Tourists

Compared with the offseason period from November to January of the following year, the per capita spending of tourists on farms in region A will increase during the peak season from May to October. It can be seen from Figure9 that the clustering integration algorithm is used to construct the rural tourism development path, which can improve the farm economy in the A region. During the peak season, in 2020, the per capita spending of farmhouse tourists in region A will be at least 636 yuan, while in 2021, it will be at least 735 yuan. This shows that the clustering algorithm is used to construct the feasibility of rural tourism development path.Figure 9
Statistical analysis chart of per capita spending of farm tourists in area A. (a) Statistical analysis chart of per capita spending of tourists on farms in region A in 2020. (b) Statistical analysis chart of per capita spending of tourists on farms in region A in 2021.
(a)(b)


## 4.4. Statistical Analysis of the Number of Tourists in the Farm

According to the data results in Figure10, compared with 2020, the number of tourists to farms in region A increased from January to December. In October, the peak season, the number of tourists in 2020 and 2021 was 12.35 million and 15.36 million, respectively. The number of tourists in 2021 is 3.01 million more than in 2020. This shows that the clustering integration algorithm is effective in constructing the development path of rural tourism.Figure 10
Statistical analysis chart of the number of farm visitors in area A. (a) Statistical analysis chart of the number of tourists to farms in region A in 2020; (b) Statistical analysis of the number of tourists to farms in region A in 2021.
(a)(b)


## 5. Discussion

Through the comparative experimental analysis of the traditional rural tourism development model (2020) and the rural tourism development model using clustering algorithm (2021), the following conclusions can be drawn:(1)
Farm tourism income: compared with the traditional rural tourism development model in 2020, the tourism income of farms in region A will be significantly improved in 2021. It has enabled the development of rural tourism in region A, and various farms have also achieved increased production and income(2)
Distribution index of scenic spots: the data shows that the establishment of scenic spots in 2021 will increase exponentially. Farms in area A have begun to build cultural villages, beautiful flower valleys, ecological parks, and farm-style houses and sightseeing parks. The rational use of the local resources of the farm not only attracts tourists from all over the world but also enables rural residents to enjoy a more comfortable and healthier home. It further drives the rural residents to be positive, united, and struggle(3)
The per capita spending of tourists: the clustering algorithm is used to construct the development path of rural tourism, which makes the village more aware of tourism and promotes the enthusiasm of tourists to experience various tourism projects. For example, tourists are more willing to stay in homestays and experience farm tour package services. This makes the per capita spending of tourists in 2021 increase to a certain extent during the peak season from May to October(4)
The number of visitors to the farm: rural tourism with beautiful environment has attracted countless city dwellers. The coordinated development path of rural tourism and big data promotes the construction of more distinctive farms, so that tourists have a different feeling for each farm, allowing travelers to have a fresh travel experienceThe whole comparative test data shows that the development path of rural tourism under the background of big data is more conducive to the development of rural tourism in terms of farm tourism income, distribution of scenic spots, per capita spending of tourists, and the number of farm tourists. It further verifies the superiority of clustering algorithm for constructing rural tourism development path.


## 6. Conclusion

This paper studies the development path and data mining mode analysis of rural tourism under the background of big data and proposes a clustering integration algorithm in the field of big data mining. It is applied to the construction of the development path of rural tourism, takes the farm tourism data of A region as the research object, and realizes the application analysis of the development path of rural tourism under the background of big data. The analysis results show that this clustering integration algorithm is effective in the construction of rural tourism development path. The development path of rural tourism is very complex and involves a wide range of areas. Due to the limited time and energy of the author, and the limitation of resources, this article has some shortcomings, such as the refinement and expansion of the development path of rural tourism. Other interference factors that affect the accuracy of the clustering integration algorithm and other factors affecting the development path of rural tourism in the context of big data are not considered.



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