# Landscape Preference of Rural Cultural Tourism Tourists Based on GIS Spatial Statistical Technology and Emotion Analysis

Authors: Wenhua Yang
Journal: Security and Communication Networks
 (2022)

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## Abstract

How to combine GIS spatial statistics technology to realize the intelligent and efficient classification of tourists’ landscape preferences is a hot spot in the direction of rural cultural tourism. Based on this, this paper studies the emotion analysis strategy based on GIS spatial statistical method in the field of landscape preference of rural cultural tourism tourists. Firstly, an intelligent and efficient classification model based on remote sensing discrete expansion algorithm is proposed. Combined with the observation strategy of common emotional rules in the landscape preference analysis of rural cultural tourism tourists, the landscape preference of rural cultural tourism tourists is processed with high quantitative emotional information, so as to realize the intelligent separation of preference, and then the error of discrete remote sensing data expansion model is corrected. Secondly, the judgment function of emotional rules is constructed, and the commonly used data layer-driven decentralized method is used in preference classification to eliminate the secondary factors in the landscape preference analysis of rural cultural tourism tourists. At the same time, the remote sensing discrete expansion algorithm is used to optimize and improve the emotional rules of high-volume emotional information based on GIS spatial statistical method. Finally, through the design of confirmatory experiments, the results show that the landscape preference analysis model based on GIS spatial statistics method and remote sensing discrete expansion algorithm is more efficient than the conventional preference classification method.


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## Body

## 1. Introduction

In recent years, the construction of rural tourism is in full swing in the country. At the same time, more and more scholars begin to pay attention to the relevant research of rural tourism. However, due to the complexity of the countryside itself and the availability of rural data, the research has certain limitations. At the same time, the research on rural tourism landscape preference is relatively scarce. In the context of the big data era, information and communication technology (ICT) has made continuous progress. At the same time, with the promotion of intelligent devices, Web2. 0 mode, the user-generated content in this mode not only provides a data source for rural tourism-related research, but also provides a convenient way to accurately study tourists’ landscape preferences. Therefore, the information with tourists’ subjective preferences such as online big data, tourists’ spatial location data, photos, and text data is very important. It is helpful to understand tourists’ understanding and needs for rural tourism landscape, so as to better show and effectively spread its regional landscape value and traditional cultural value. In the current remote sensing classification process, there are often many wrong and missing points. The classification result is limited by the spatial resolution of remote sensing image itself. The classification accuracy is not high due to the existence of the phenomenon of different spectra of the same object and the same spectrum of foreign objects.The research on the landscape preference analysis of rural cultural tourism tourists in China has been carried out for a long time, and there are many research directions involved. From the perspective of tourists’ landscape preference, the mainstream research directions include landscape review rate, the dynamic relationship between season and tourist flow, and the internal relationship between altitude and tourist group characteristics [1]. In the conventional method of analyzing the landscape preference of rural cultural tourism tourists, the quantification of emotion and preference is generally realized through the classification of emotion and the normalized statistical method of GIS space at the key nodes of tourists’ landscape preference [2]. The core content of landscape preference analysis and research of rural cultural tourism tourists is the emotional difference in the process of tourists’ preference difference for landscape, which is of great significance to promote the intelligent development of the combination of GIS spatial statistical method application optimization and high-volume emotional information [3]. At present, in the previous research on the landscape preference analysis of rural cultural tourism tourists, it is difficult to have good universality and wide coverage [4].Based on this background, this paper studies the application of high quantitative emotional information analysis model based on GIS spatial statistical method in the quantification of landscape preference of rural cultural tourism tourists, which is mainly divided into four chapters. Chapter 1 introduces the research background, research necessity, and chapter arrangement of the paper; chapter 2 introduces the research status of emotion preference classification model, and analyzes its emotion and tourism landscape preference. In Chapter 3, the emotion rule judgment function combined with remote sensing discrete expansion algorithm is constructed. According to the different landscape preferences of rural cultural tourism tourists, the tourists’ landscape preference is quantitatively separated and then combined with the differences of emotional expression to realize multidimensional clustering. Chapter 4 tests the application of the emotional preference classification model combined with remote sensing discrete expansion algorithm in rural cultural tourism, so as to verify its feasibility and accuracy. The experimental results show that compared with the traditional analysis methods of tourists’ landscape preference, the high quantitative emotion information analysis model combined with remote sensing discrete expansion algorithm proposed in this study has higher separation accuracy and wider application range.The innovation of this paper is to propose an emotional preference classification model combining GIS spatial statistics method and remote sensing discrete expansion algorithm for the analysis of rural cultural and tourism tourists’ landscape preference. This method can carry out quantitative cluster analysis on different types of landscape preferences for different types of tourists, and intelligently and efficiently classify the identified landscape preference judgment process of rural cultural tourism tourists in combination with emotion rule judgment function and GIS spatial statistical method, so as to realize the preference quantification process of “regional limitation of rural cultural tourism—cngis spatial statistical method model—quantification of tourist trajectory—emotion matching—accurate classification.”


## 2. State of the Art

Domestic and foreign scholars’ research on the analysis of landscape preference of rural cultural tourism tourists mainly focuses on the change process of tourists’ trajectory, and less research with cross-characteristics is carried out from the perspective of GIS spatial statistical method and remote sensing discrete expansion algorithm [5]. At present, in the research process of landscape preference of different types of tourists, because different types of tourist groups have different preference characteristics, there are often problems of unreliable preference results and low value [6]. Al-Aamri et al. found that there is a great correlation between the current rural cultural tourism tourists’ preference for landscape and mood. Therefore, they proposed a landscape preference analysis model of tourism tourists to remove the impact of landscape noise, which can effectively feed back the emotional information of different tourists [7]. By analyzing the geographical differences of rural cultural tourism, Baker A and other scholars put forward a high-value matching analysis model of landscape preference based on the characteristics of tourists’ landscape preference according to the age differences of different tourists, but there are large prominent errors [8]. The research results of Lynch show that the tourist landscape preference method based on ad hoc network strategy has better result reliability than the conventional rural cultural tourism tourist landscape preference analysis method, but it cannot efficiently combine the emotional information of rural cultural tourism tourists to achieve high-value strategy matching [9]. Opitz et al. found that the geographical location and temperature of different groups of tourists have a great impact on tourists in the process of landscape visit. Therefore, combined with remote sensing discrete expansion algorithm, a three-dimensional spatial analysis model of rural cultural tourism tourists’ landscape preference is proposed, which can effectively quantify the impact of geographical location on tourists’ landscape preference [10]. Combined with the existing idea of remote sensing discrete expansion algorithm, scholars such as Loc et al. carried out strategic iterative analysis on the process of landscape preference analysis of rural cultural tourism tourists, proposed a remote sensing discrete expansion algorithm based on the idea of self-optimization system, and realized the data stacking and value analysis of rural cultural tourism tourists’ landscape preference by using the value analysis dimension of rural cultural tourism tourists’ landscape dwell time. However, the degree of emotion analysis strategy is low [11]. Setia et al. put forward a new “self-network format” landscape preference analysis model of rural cultural tourism tourists through the research and analysis of the differences of different rural cultural tourisms in the reception of tourists and verified that the “self-network format” analysis model has good matching feature efficiency through experiments on several different types of tourism tourist groups. It has good reliability in the analysis of landscape preference of rural cultural tourism tourists [12]. In order to improve the reliability and social value of the landscape preference of rural cultural tourism tourists, Ahmed et al. put forward a landscape preference analysis rule of rural cultural tourism tourists with high social value, which can effectively improve the speed of preference analysis according to the characteristics of tourist visiting routes of different rural cultural tourism scenic spots in different seasons, with the idea of high group nonvalue statistical method in physical space as the core [13].To sum up, it can be seen that the current landscape preference analysis system of rural cultural tourism tourists with specific landscape preference as the database generally has the problems of poor recognition effect, low recognition accuracy, poor stability, and low data utilization rate in practical application [14–16]. On the other hand, in the existing landscape preference analysis system of rural cultural tourism tourists, the vast majority of identification methods can only identify a single rural cultural tourism and cannot identify rural cultural tourism with obvious differences, so they do not have the characteristics of intelligence [17–19]. In addition, the utilization rate and data mining effect of the obtained landscape preference data of rural cultural tourism tourists in the process of identification are also very poor [18].


## 3. Methodology

### 3.1. Application of Remote Sensing Discrete Expansion Algorithm in Tourists’ Landscape Preference

GIS spatial statistics method is to obtain the image information of large-scale areas by using remote sensing technology, splice and convert these image phenomena into data information, and then discrete extraction, so as to realize the data information statistics of GIS spatial location, carry out high-value statistics of effective data, and realize the summary and classification of specific data information [20]. Remote sensing discrete expansion algorithm is an intelligent calculation method based on GIS spatial statistical method strategy to discretize different spatial information and realize the high-value analysis and quantitative representation of different spatial statistical data by setting the gradient rules of optimal value and optimal target solution [21]. The conventional remote sensing discrete expansion algorithm realizes the coupling solution of its data through the dispersion analysis and matching degree control of super-large-scale spatial data, internal interpretation, and value matching clustering [22]. In the remote sensing discrete expansion algorithm combined with the idea of GIS spatial statistics method, based on the remote sensing discrete expansion algorithm and the spatial three-dimensional processing idea of GIS spatial statistics method, a round of fission classification based on the top-down strategy is carried out for the spatial location data group and then combined with the high-intensity matching error analysis strategy to realize the high reliability of the results.Based on the above ideas, when constructing the landscape preference analysis modal model of rural cultural tourism tourists, this study combines the remote sensing discrete expansion algorithm and the emotional preference classification model. In the process of data space analysis and location modeling, first discretize the location information of tourists in rural cultural tourism, and then combine its internal mathematical connection map and realize the spatial location modeling and value degree classification required for the landscape preference analysis process of rural cultural tourism tourists. Finally, according to the known landscape emotional preference data collection database of rural cultural tourism tourists, realize the high-value analysis of the value degree of tourist attractions, make statistics in a quantitative form, and output the final preference classification results.


### 3.2. Construction Process of Landscape Preference Analysis Model of Rural Cultural Tourism Tourists

After the thought modeling of GIS spatial statistical method strategy based on remote sensing discrete expansion algorithm, it is also necessary to analyze its emotion, so as to further improve the value matching analysis strategy of different types of landscape preference. From the development practice of rural leisure tourism, the research on the emotional elements of tourists’ tourism motivation is the practical need to improve the value of rural leisure tourism [23]. This paper attempts to establish the emotional observation indicators of rural leisure tourists and analyzes the impact of “return” emotion on the whole process of tourists’ consumption experience before, during, after, and before traveling again through the indicators such as willingness. Firstly, by combining the remote sensing discrete expansion algorithm of rural cultural tourism geographic location information thesaurus and GIS spatial statistical method strategy, three characteristic parameters related to the landscape preference analysis of rural cultural tourism tourists and the landscape preference of GIS spatial statistical method are selected, and an emotion normalization analysis system of rural cultural tourism tourists based on farilai emotion regression is proposed [24]. Through the research on the analysis process of various emotional expressions, emotional switching frequency, and preference analysis of tourists in the process of rural cultural tourism, the visual management and analysis of different landscape preferences are realized. The construction process of the landscape preference data analysis model of rural cultural tourism tourists is shown in Figure 1.Figure 1
The construction process of the data analysis model for rural cultural tourism tourists’ landscape preference data.In the process of analyzing the landscape preference of rural cultural tourism tourists, it can be divided into the following links for evaluation and analysis with different emphases:The first step is to determine the location information and area difference of rural cultural tourism in different landscapes. In this tourist emotion classification model, firstly, different types of emotion observers are used to realize the emotional difference characteristics represented by tourists in different scenic spots, realize the normalized classification of different emotion types of tourists, and then use the idea of quantitative expression to realize the ranking of their value. Then, according to the dimensional differences of scenic spots under different types of rural cultural tourism, the evaluation value of landscape preference emotion of rural cultural tourism tourists, the characteristic value of GIS spatial statistical method and the emotion normalization functionAx are solved, and the expression is(1)Ax=1−P1cx−1+P2cx+1P1x/c−1σP1cx−1−P2cx−2P2x/cσ.The spatial data information of scenic spots is represented byP2, the temporal information is represented by P1, the emotional starting point is c, the emotional disturbance coefficient is σ, and the standard quantization value is 1.In the second step, in the remote sensing discrete expansion algorithm, it is also necessary to calculate the high-value matching of the spatial characteristics of scenic spots in rural cultural tourism, so the emotion normalization functionA′x at this time is(2)A′x=1−βc5.1x+αc3.2xP1c+P2c/txϕ1+αc5.1x−βc3.2xP1c−P2c/txϕ.Among them, the discrete spatial data information of scenic spots is represented byα, the temporal information is represented by β, the emotional starting point is c, the emotional disturbance coefficient is ϕ, and the standard quantization value is 1. After normalizing the GIS spatial function of scenic spots in rural cultural tourism, it is also necessary to prevent disturbance in combination with emotion degree. The theory of consumption motivation points out that consumers’ consumption behavior is dominated by their motivation. Motivation comes from need, which is a lack of objective stimuli acting on the human brain through human senses. The diversity of needs determines the diversity of motives. The theory of consumption motivation shows that tourists’ emotion not only affects tourists’ decision-making of destination, but also is the basis of tourists’ subsequent consumption behavior.At this time, the emotion normalization functionA′′x is(3)A′′x=βc11x+αc22xαc11x−βc22xP1c−P2c/txϕ−βc5.1x+αc3.2xP1c+P2c/txϕ1+αc5.1x−βc3.2xP1c−P2c/txϕ.After completing the anti-disturbance, it is also necessary to determine its spatial location. The classification process of landscape preference of rural cultural tourism tourists by intelligent and efficient emotion classification model is shown in Figure2.Figure 2
The classification process of landscape preference of rural cultural tourism tourists by intelligent and efficient emotional classification model.


### 3.3. The Optimization Process of Landscape Preference Analysis Model and Emotion Rule Judgment Function of Rural Cultural Tourism Tourists

In order to further study the efficiency of landscape preference analysis of rural cultural tourism tourists, it is also necessary to judge and quantify the emotional process corresponding to different tourist groups, so its corresponding emotional characteristics are very important. Before revising the judgment function of emotional rules, in order to further test the shortcomings of the existing judgment function, the statistical data of landscape pedestrian flow, preference types, and emotional characteristics of three types of rural cultural tourism tourists at different stages are selected for simulation, and the results are shown in Figure3.Figure 3
Simulation results of the statistical data of three types of landscape traffic flow, preference types, and emotional characteristics at different stages.It can be seen from the simulation results in Figure3 that as the number of emotional changes of tourism tourists in rural cultural tourism increases in different time periods, the corresponding landscape preference is also very different. This is because the emotional evaluation rules of conventional strategy function are adopted in the landscape preference analysis model of rural cultural tourism tourists, and these rules will change with the number of emotional changes. It has different types of value matching correlation effects on different emotional change data.Based on the above, different emotion statistical factors are adopted and different weight values are allocated (the higher the favorite emotion is, the higher the weight is). At this time, the simulation results of the optimization process of rural cultural tourism tourists’ landscape preference under different emotion strategies are shown in Figure4.Figure 4
Simulation results of the optimization process of rural cultural tourism tourists’ landscape preference under different emotional strategies.It can be seen from Figure4 that under the landscape human flow, preference types, and emotional characteristics at different stages, the simulation results are better than the emotional analysis results without optimization analysis, and with the increase of the number of layers of GIS spatial statistical methods, it also has a large distinction between the change value of emotional rules and the internal correlation value of landscape preference of rural cultural tourism tourists. This is because in the process of analyzing the data of different types of tourism tourists by GIS spatial statistical method, the emotion optimization rule model with variable weight proposed in this study will be superimposed to varying degrees according to the emotional differences of different types of rural cultural and tourism tourists. The decision function Bmc3x of variable weight emotion rule under the strategy of three-dimensional GIS spatial statistics method is(4)Bmc3x=1+W1xBmc2+W2xBmc1W1+W2−W1+W2Bmc2+Bmc1,x is the landscape stay data of rural cultural tourism tourists, and W is the emotional reference constant. The emotion rule judgment function Bmcnx under the idea of variable weight is(5)Bmcnx=Bmc2W1+Bmc1W2W2Bmc2+Bmc1W1,x is the landscape stay data of rural cultural tourism tourists, and W is the emotional reference constant. At this time, the corresponding emotion rule verification function Qx is(6)Qx=1−Bmc2W1+Bmc1W2/W2Bmc2+Bmc1W11+W1xBmc2+W2xBmc1/W1+W2.


### 3.4. Classification of Landscape Preference Data of Rural Cultural Tourism Tourists by Intelligent and Efficient Classification Model

In the landscape preference analysis model of rural cultural and tourism tourists based on the emotion preference classification model, the corresponding emotion rule judgment function can also be optimized from the peripheral landscape structure [25]. Therefore, according to the remote sensing discrete expansion algorithm and the idea of high-value emotion classification function, the data decomposition idea of the remote sensing discrete expansion algorithm is applied to the GIS spatial statistics method. Realize intelligent and high-intensity classification of landscape preference data of different tourist groups, and set the standard emotion degree as 0.8 based on GIS spatial location statistics. The classification simulation results of landscape preference are shown in Figure 5.Figure 5
Classification simulation results of landscape preference based on GIS spatial location statistics.It can be seen from Figure5 that in the process of analyzing landscape preference data with GIS spatial statistical methods with different emotional value degrees, the reliability and value of classification will change greatly with the differences of different types of preferences. This is because after analyzing the classification law, GIS spatial statistical method will solve the secondary coincidence, so the corresponding differences will be expanded. It is assumed that the coincidence function is represented by tx. The affective value function is represented by yx, and the classification relevance of different functions is represented by cx; then,(7)rx=txα+yxβ/α+β1+αβ.The classification functionDx and normalization function Fx of emotional value are(8)Dx=βQxαtx+βyx,Fx=Axtxα−Dxyxβαtxα+βyxβ.The disturbance functionZx and reliability function Xx before and after calculation are(9)Zx=Dx+1xα+Ax−1xβ,Xx=αAx+1+βDx−1Fx+1.The disturbance functionZ′x and reliability function X′x after optimization are(10)Z′x=Dx+1xα+Ax−1xβDx,X′x=1+αAx+1+βDx−1Fx−1+Fx+1.The nonoptimized preference classification functionRx and the optimized preference classification function R′x are(11)Rx=5αxα+3βxβ4αx+7βxβ,M′x=αxββ+3α2xα+3xβ/5αx+3βxβ3xα−7xβ.In the above formula,x is GIS spatial data processing information, and α and β are emotion dimension vectors.


## 3.1. Application of Remote Sensing Discrete Expansion Algorithm in Tourists’ Landscape Preference

GIS spatial statistics method is to obtain the image information of large-scale areas by using remote sensing technology, splice and convert these image phenomena into data information, and then discrete extraction, so as to realize the data information statistics of GIS spatial location, carry out high-value statistics of effective data, and realize the summary and classification of specific data information [20]. Remote sensing discrete expansion algorithm is an intelligent calculation method based on GIS spatial statistical method strategy to discretize different spatial information and realize the high-value analysis and quantitative representation of different spatial statistical data by setting the gradient rules of optimal value and optimal target solution [21]. The conventional remote sensing discrete expansion algorithm realizes the coupling solution of its data through the dispersion analysis and matching degree control of super-large-scale spatial data, internal interpretation, and value matching clustering [22]. In the remote sensing discrete expansion algorithm combined with the idea of GIS spatial statistics method, based on the remote sensing discrete expansion algorithm and the spatial three-dimensional processing idea of GIS spatial statistics method, a round of fission classification based on the top-down strategy is carried out for the spatial location data group and then combined with the high-intensity matching error analysis strategy to realize the high reliability of the results.Based on the above ideas, when constructing the landscape preference analysis modal model of rural cultural tourism tourists, this study combines the remote sensing discrete expansion algorithm and the emotional preference classification model. In the process of data space analysis and location modeling, first discretize the location information of tourists in rural cultural tourism, and then combine its internal mathematical connection map and realize the spatial location modeling and value degree classification required for the landscape preference analysis process of rural cultural tourism tourists. Finally, according to the known landscape emotional preference data collection database of rural cultural tourism tourists, realize the high-value analysis of the value degree of tourist attractions, make statistics in a quantitative form, and output the final preference classification results.


## 3.2. Construction Process of Landscape Preference Analysis Model of Rural Cultural Tourism Tourists

After the thought modeling of GIS spatial statistical method strategy based on remote sensing discrete expansion algorithm, it is also necessary to analyze its emotion, so as to further improve the value matching analysis strategy of different types of landscape preference. From the development practice of rural leisure tourism, the research on the emotional elements of tourists’ tourism motivation is the practical need to improve the value of rural leisure tourism [23]. This paper attempts to establish the emotional observation indicators of rural leisure tourists and analyzes the impact of “return” emotion on the whole process of tourists’ consumption experience before, during, after, and before traveling again through the indicators such as willingness. Firstly, by combining the remote sensing discrete expansion algorithm of rural cultural tourism geographic location information thesaurus and GIS spatial statistical method strategy, three characteristic parameters related to the landscape preference analysis of rural cultural tourism tourists and the landscape preference of GIS spatial statistical method are selected, and an emotion normalization analysis system of rural cultural tourism tourists based on farilai emotion regression is proposed [24]. Through the research on the analysis process of various emotional expressions, emotional switching frequency, and preference analysis of tourists in the process of rural cultural tourism, the visual management and analysis of different landscape preferences are realized. The construction process of the landscape preference data analysis model of rural cultural tourism tourists is shown in Figure 1.Figure 1
The construction process of the data analysis model for rural cultural tourism tourists’ landscape preference data.In the process of analyzing the landscape preference of rural cultural tourism tourists, it can be divided into the following links for evaluation and analysis with different emphases:The first step is to determine the location information and area difference of rural cultural tourism in different landscapes. In this tourist emotion classification model, firstly, different types of emotion observers are used to realize the emotional difference characteristics represented by tourists in different scenic spots, realize the normalized classification of different emotion types of tourists, and then use the idea of quantitative expression to realize the ranking of their value. Then, according to the dimensional differences of scenic spots under different types of rural cultural tourism, the evaluation value of landscape preference emotion of rural cultural tourism tourists, the characteristic value of GIS spatial statistical method and the emotion normalization functionAx are solved, and the expression is(1)Ax=1−P1cx−1+P2cx+1P1x/c−1σP1cx−1−P2cx−2P2x/cσ.The spatial data information of scenic spots is represented byP2, the temporal information is represented by P1, the emotional starting point is c, the emotional disturbance coefficient is σ, and the standard quantization value is 1.In the second step, in the remote sensing discrete expansion algorithm, it is also necessary to calculate the high-value matching of the spatial characteristics of scenic spots in rural cultural tourism, so the emotion normalization functionA′x at this time is(2)A′x=1−βc5.1x+αc3.2xP1c+P2c/txϕ1+αc5.1x−βc3.2xP1c−P2c/txϕ.Among them, the discrete spatial data information of scenic spots is represented byα, the temporal information is represented by β, the emotional starting point is c, the emotional disturbance coefficient is ϕ, and the standard quantization value is 1. After normalizing the GIS spatial function of scenic spots in rural cultural tourism, it is also necessary to prevent disturbance in combination with emotion degree. The theory of consumption motivation points out that consumers’ consumption behavior is dominated by their motivation. Motivation comes from need, which is a lack of objective stimuli acting on the human brain through human senses. The diversity of needs determines the diversity of motives. The theory of consumption motivation shows that tourists’ emotion not only affects tourists’ decision-making of destination, but also is the basis of tourists’ subsequent consumption behavior.At this time, the emotion normalization functionA′′x is(3)A′′x=βc11x+αc22xαc11x−βc22xP1c−P2c/txϕ−βc5.1x+αc3.2xP1c+P2c/txϕ1+αc5.1x−βc3.2xP1c−P2c/txϕ.After completing the anti-disturbance, it is also necessary to determine its spatial location. The classification process of landscape preference of rural cultural tourism tourists by intelligent and efficient emotion classification model is shown in Figure2.Figure 2
The classification process of landscape preference of rural cultural tourism tourists by intelligent and efficient emotional classification model.


## 3.3. The Optimization Process of Landscape Preference Analysis Model and Emotion Rule Judgment Function of Rural Cultural Tourism Tourists

In order to further study the efficiency of landscape preference analysis of rural cultural tourism tourists, it is also necessary to judge and quantify the emotional process corresponding to different tourist groups, so its corresponding emotional characteristics are very important. Before revising the judgment function of emotional rules, in order to further test the shortcomings of the existing judgment function, the statistical data of landscape pedestrian flow, preference types, and emotional characteristics of three types of rural cultural tourism tourists at different stages are selected for simulation, and the results are shown in Figure3.Figure 3
Simulation results of the statistical data of three types of landscape traffic flow, preference types, and emotional characteristics at different stages.It can be seen from the simulation results in Figure3 that as the number of emotional changes of tourism tourists in rural cultural tourism increases in different time periods, the corresponding landscape preference is also very different. This is because the emotional evaluation rules of conventional strategy function are adopted in the landscape preference analysis model of rural cultural tourism tourists, and these rules will change with the number of emotional changes. It has different types of value matching correlation effects on different emotional change data.Based on the above, different emotion statistical factors are adopted and different weight values are allocated (the higher the favorite emotion is, the higher the weight is). At this time, the simulation results of the optimization process of rural cultural tourism tourists’ landscape preference under different emotion strategies are shown in Figure4.Figure 4
Simulation results of the optimization process of rural cultural tourism tourists’ landscape preference under different emotional strategies.It can be seen from Figure4 that under the landscape human flow, preference types, and emotional characteristics at different stages, the simulation results are better than the emotional analysis results without optimization analysis, and with the increase of the number of layers of GIS spatial statistical methods, it also has a large distinction between the change value of emotional rules and the internal correlation value of landscape preference of rural cultural tourism tourists. This is because in the process of analyzing the data of different types of tourism tourists by GIS spatial statistical method, the emotion optimization rule model with variable weight proposed in this study will be superimposed to varying degrees according to the emotional differences of different types of rural cultural and tourism tourists. The decision function Bmc3x of variable weight emotion rule under the strategy of three-dimensional GIS spatial statistics method is(4)Bmc3x=1+W1xBmc2+W2xBmc1W1+W2−W1+W2Bmc2+Bmc1,x is the landscape stay data of rural cultural tourism tourists, and W is the emotional reference constant. The emotion rule judgment function Bmcnx under the idea of variable weight is(5)Bmcnx=Bmc2W1+Bmc1W2W2Bmc2+Bmc1W1,x is the landscape stay data of rural cultural tourism tourists, and W is the emotional reference constant. At this time, the corresponding emotion rule verification function Qx is(6)Qx=1−Bmc2W1+Bmc1W2/W2Bmc2+Bmc1W11+W1xBmc2+W2xBmc1/W1+W2.


## 3.4. Classification of Landscape Preference Data of Rural Cultural Tourism Tourists by Intelligent and Efficient Classification Model

In the landscape preference analysis model of rural cultural and tourism tourists based on the emotion preference classification model, the corresponding emotion rule judgment function can also be optimized from the peripheral landscape structure [25]. Therefore, according to the remote sensing discrete expansion algorithm and the idea of high-value emotion classification function, the data decomposition idea of the remote sensing discrete expansion algorithm is applied to the GIS spatial statistics method. Realize intelligent and high-intensity classification of landscape preference data of different tourist groups, and set the standard emotion degree as 0.8 based on GIS spatial location statistics. The classification simulation results of landscape preference are shown in Figure 5.Figure 5
Classification simulation results of landscape preference based on GIS spatial location statistics.It can be seen from Figure5 that in the process of analyzing landscape preference data with GIS spatial statistical methods with different emotional value degrees, the reliability and value of classification will change greatly with the differences of different types of preferences. This is because after analyzing the classification law, GIS spatial statistical method will solve the secondary coincidence, so the corresponding differences will be expanded. It is assumed that the coincidence function is represented by tx. The affective value function is represented by yx, and the classification relevance of different functions is represented by cx; then,(7)rx=txα+yxβ/α+β1+αβ.The classification functionDx and normalization function Fx of emotional value are(8)Dx=βQxαtx+βyx,Fx=Axtxα−Dxyxβαtxα+βyxβ.The disturbance functionZx and reliability function Xx before and after calculation are(9)Zx=Dx+1xα+Ax−1xβ,Xx=αAx+1+βDx−1Fx+1.The disturbance functionZ′x and reliability function X′x after optimization are(10)Z′x=Dx+1xα+Ax−1xβDx,X′x=1+αAx+1+βDx−1Fx−1+Fx+1.The nonoptimized preference classification functionRx and the optimized preference classification function R′x are(11)Rx=5αxα+3βxβ4αx+7βxβ,M′x=αxββ+3α2xα+3xβ/5αx+3βxβ3xα−7xβ.In the above formula,x is GIS spatial data processing information, and α and β are emotion dimension vectors.


## 4. Result Analysis and Discussion

### 4.1. Verification Experiment of Landscape Preference Analysis Model of Rural Cultural Tourism Tourists Based on GIS Spatial Statistical Method

In order to verify the authenticity and value of the landscape preference analysis model of rural cultural tourism tourists based on GIS spatial statistical method, this study conducted an experiment on the authenticity data. The data used in the experiment are high matching truth data sets at different levels, and they are landscape data sets in different types of rural cultural tourism. Their information dimensions include spatial information, time information, and location information. The corresponding experimental results of three-dimensional data preference analysis and three-dimensional characterization are shown in Figure6.Figure 6
Experimental results of preference analysis and stereoscopic characterization of 3D data.According to the experimental results in Figure6, the landscape preference analysis model of rural cultural tourism tourists based on GIS spatial statistical method gradually shows a relatively small change law (color change degree) with the increase of the number of experiments in the process of three-dimensional representation of preference analysis of different tourists. This is because during the experiment, the intelligent classification model of landscape preference of rural cultural tourism tourists based on GIS spatial statistical method will process different types of databases with multi-dimensional vectors after completing the emotional analysis of tourists. Through the intelligent representation of vectors, the quantitative performance of the landscape preference of different tourists can be realized. Therefore, the preference analysis effect of rural cultural tourism tourists’ landscape preference data is more convincing.


### 4.2. Analysis of Experimental Results

This paper investigates the different dimensions of tourists, such as gender, age, education level, occupation, income, and rural tourism experience. This paper analyzes the differences of “return” emotion of rural tourists. The results showed that there were significant differences in occupation, income, and educational level between the two groups. In other words, occupation and income are the core factors affecting the “return” emotion of rural tourism. Education level also has a certain impact. Gender, age, and other factors did not have a significant impact on the “return” emotion. This shows that the “return” emotion of rural tourists is formed by their personal education level in their long-term work and life. And once this emotion is formed, it is difficult to change easily, and it will form stable emotional characteristics in a long life.In order to further analyze the effectiveness of the experimental results, after completing the preliminary experimental analysis of the landscape data types of rural cultural tourism tourists, it is also necessary to make an in-depth objective evaluation of their value and preference. Therefore, in the optimized preference evaluation method, the secondary verification and quantitative evaluation analysis of the experimental results are realized by using the commonly used experimental error analysis strategy. The verification results are shown in Figure7.Figure 7
Secondary verification of experimental results and verification results of quantitative evaluation analysis.It can be seen from Figure7 that for the discrete rural cultural tourism tourists’ landscape data and the continuous rural cultural tourism tourists’ landscape data group, the intelligent and efficient classification model of rural cultural tourism tourists’ landscape preference based on the remote sensing discrete expansion algorithm of GIS spatial statistical method strategy can effectively improve the accuracy of tourists’ landscape preference and is in a relatively stable range. At the same time, the multi-dimensional GIS spatial statistical method can also effectively improve its internal preference analysis speed and greatly reduce the demand for the original landscape data set.


## 4.1. Verification Experiment of Landscape Preference Analysis Model of Rural Cultural Tourism Tourists Based on GIS Spatial Statistical Method

In order to verify the authenticity and value of the landscape preference analysis model of rural cultural tourism tourists based on GIS spatial statistical method, this study conducted an experiment on the authenticity data. The data used in the experiment are high matching truth data sets at different levels, and they are landscape data sets in different types of rural cultural tourism. Their information dimensions include spatial information, time information, and location information. The corresponding experimental results of three-dimensional data preference analysis and three-dimensional characterization are shown in Figure6.Figure 6
Experimental results of preference analysis and stereoscopic characterization of 3D data.According to the experimental results in Figure6, the landscape preference analysis model of rural cultural tourism tourists based on GIS spatial statistical method gradually shows a relatively small change law (color change degree) with the increase of the number of experiments in the process of three-dimensional representation of preference analysis of different tourists. This is because during the experiment, the intelligent classification model of landscape preference of rural cultural tourism tourists based on GIS spatial statistical method will process different types of databases with multi-dimensional vectors after completing the emotional analysis of tourists. Through the intelligent representation of vectors, the quantitative performance of the landscape preference of different tourists can be realized. Therefore, the preference analysis effect of rural cultural tourism tourists’ landscape preference data is more convincing.


## 4.2. Analysis of Experimental Results

This paper investigates the different dimensions of tourists, such as gender, age, education level, occupation, income, and rural tourism experience. This paper analyzes the differences of “return” emotion of rural tourists. The results showed that there were significant differences in occupation, income, and educational level between the two groups. In other words, occupation and income are the core factors affecting the “return” emotion of rural tourism. Education level also has a certain impact. Gender, age, and other factors did not have a significant impact on the “return” emotion. This shows that the “return” emotion of rural tourists is formed by their personal education level in their long-term work and life. And once this emotion is formed, it is difficult to change easily, and it will form stable emotional characteristics in a long life.In order to further analyze the effectiveness of the experimental results, after completing the preliminary experimental analysis of the landscape data types of rural cultural tourism tourists, it is also necessary to make an in-depth objective evaluation of their value and preference. Therefore, in the optimized preference evaluation method, the secondary verification and quantitative evaluation analysis of the experimental results are realized by using the commonly used experimental error analysis strategy. The verification results are shown in Figure7.Figure 7
Secondary verification of experimental results and verification results of quantitative evaluation analysis.It can be seen from Figure7 that for the discrete rural cultural tourism tourists’ landscape data and the continuous rural cultural tourism tourists’ landscape data group, the intelligent and efficient classification model of rural cultural tourism tourists’ landscape preference based on the remote sensing discrete expansion algorithm of GIS spatial statistical method strategy can effectively improve the accuracy of tourists’ landscape preference and is in a relatively stable range. At the same time, the multi-dimensional GIS spatial statistical method can also effectively improve its internal preference analysis speed and greatly reduce the demand for the original landscape data set.


## 5. Conclusion

This paper studies the analysis strategy of landscape preference emotion of rural cultural tourism tourists based on GIS spatial statistical method. An intelligent and efficient classification model based on remote sensing discrete extension algorithm is proposed. The results show that the landscape preference analysis model based on GIS spatial statistical method and remote sensing discrete expansion algorithm is more effective than the traditional preference classification method. The remote sensing discrete expansion algorithm based on GIS spatial statistical method can intelligently and efficiently analyze the landscape preference classification of rural cultural tourism tourists. It can effectively improve the accuracy of tourists’ landscape preference and is in a relatively stable range. At the same time, multi-dimensional GIS spatial statistical method can also effectively improve the speed of internal preference analysis and greatly reduce the demand for the original landscape data set.However, this study only considers the impact of tourism location information of rural cultural tourism on landscape value and does not consider its internal impact on landscape emotional love in different cultural tourism scenes. Therefore, more in-depth research can be carried out.



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*Source: 4770803-2022-07-08.xml*