Research on the construction of rural complex in the context of rural revitalization based on FAHP

Abstract

Rural revitalization is an attempt by China’s rural reform plan. Among them, the pastoral complex emphasizes the integration of agriculture and tourism, extends the agricultural chain, and integrates health care, technology, tourism, creativity, and leisure. However, the pastoral complex is faced with solving rural-urban integration in the context of the current rural revitalization strategy, involving complex factors such as land, ecological, cultural, and social issues. Therefore, this study analyzes the complex factors affecting the pastoral complex. Fuzzy calculation theory is introduced in the pastoral complex to discuss the pastoral complex system’s operational characteristics and further explore how to build an adaptive system evaluation system at different system levels. Considering that the process needs to consider several Conflicting factors from qualitative to quantitative, to deal with the uncertainty of human judgment in the evaluation process, the process uses fuzzy analytical hierarchy process to obtain the weight of each factor and understand the degree of influence of each factor. The research results show that various factors have different degrees of influence on the pastoral complex. Therefore, in the complex pastoral process, more attention should be paid to the operation mechanism factors to make the complex pastoral system more scientific.

Keywords

Fuzzy calculation rural revitalization pastoral complex FAHP

1 Introduction

"Rural revitalization” is the general requirements of “industrial integration, ecological livability, rural civilization, effective governance, and affluent life” put forward in the report of the 19th National Congress of the Communist Party of China. The No. 1 document of the Central Committee in 2017 emphasized: “With farmers’ cooperatives as the main carrier, support the construction of qualified rural areas, allow farmers to participate fully and benefit, and integrate circular agriculture, creative agriculture, and farming experience [1].” The proposal of the pastoral complex is an iteration of the concepts of rural tourism and agricultural culture industry such as “leisure agricultural park", “rural tourism body” and “modern agricultural park". It is a comprehensive agricultural tourism industry that has emerged at the current rural industrial economic development stage, landed the project. The pastoral complex’s ideal vision is to open up rural life and farming scenes to urban residents and introduce suitable formats to integrate urban and rural life and work methods. At the same time, it is hoped that the traditional rural aesthetic values can be inherited in a purposeful and planned manner, to achieve both tradition and modernity. Open and mutually supportive modern houses and traditional settlements coordinated lifestyle. Simultaneously, the pastoral complex has a symbiosis of industries, a complete system, and a combination of sports and agricultural production to maintain an ideal living condition that is more ecological and livable. However, due to the persistence and protection of land policies, the lack of experience in the development and utilization of rural projects in the current policy system, the narrow rural investment and financing mechanism, and the limitations of traditional farms or tourist attractions, the pastoral complex projects are often inefficient The operating status, the problems can be summarized in two aspects: First, under the implementation of the urban-rural dual system, resource utilization constraints caused by the differentiated development of resource allocation (mainly divided into three aspects: limited resource utilization, excessive resource utilization, and inefficient resource utilization) On the other hand, with the emphasis on environmental issues and the promotion of the construction of ecological industrial parks, the healthy development of the pastoral complex needs to take into account multiple strategic goals such as environment, industry, talents, culture, and organizational revitalization. Therefore, this thesis combines the relevant theories and methods of industrial ecology, fuzzy system theory and system analysis to explore the core elements, structure, and functions of the fuzzy computing system of the rural complex under the strategic goal of rural revitalization, and how to ensure a well-structured and functional system Operating mechanism.

This study attributed the non-linear interaction of individuals’ symbiosis in different industries such as agriculture, picking, health care, homestay, cultural and creative industries to their active adaptability to the market and the environment, and the different material flows, information flows and funds generated during the process. The mechanism of interaction among energy-generating elements such as flow. According to the analysis path, the leading organization of the pastoral complex under the rural revitalization scenario includes the recycling of economic and environmental resources between various types of rural tourism and modern agriculture, such as homestays, modern agricultural, industrial parks, farmhouses, leisure agricultural areas, forest health care bases, etc. It can be divided into subsystems such as industry, society, space, culture, and economies of scale through the establishment of various shared markets and extended land use. The pastoral complex’s core function is to realize the deep integration of agriculture and the secondary and tertiary industries. Among them, the “pastoral” is the industrial ecological environment, which belongs to the spatial category; the “complex” is the deduction of the concept of “urban complex” and represents the industry with multiple functions. The symbiosis model also means integrating multiple business forms of relevant stakeholders; the essence of the operation of the pastoral complex system is to solve the multi-level relationship between production and consumption, products and demand, and people and the environment. Among them, from the one-way linear production model of agriculture to the pastoral complex model, which involves the compounding of functions and the diversification of operating entities, it is necessary to build a complex circular economy network model in the region, and finally realize the construction of a large system platform, including: pastoral integration It is composed of the supporting matrix of symbiosis entity community, technology, information network, etc., market environment and another relationship matrix, and relying on the support of role organizations composed of tourism operators, farmers, tourists, farmers, related associations, and government supervision departments. The whole system, promotion and realization; starting from this cognitive framework, the need for research on the construction of the pastoral complex system can be formulated.

2 Literature review

Laarhoven & Pedrycz modified the method proposed by De Grann [2, 3], using fuzzy set theory (Fuzzy Sets Theory) and fuzzy arithmetic (Fuzzy Arithmetic), and proposed a set based on the expansion principle and approximation theory. The calculation method of fuzzy triangular numbers (TFN) replaces the explicit value produced by pairwise comparison in the hierarchical analysis method. The logarithmic least square method is used to calculate each criterion’s fuzzy weight to improve the analysis and application of the traditional AHP method. Buckley [4] is based on the inaccuracy problem of traditional AHP and Saaty [5] is used to calculate the weight method is difficult to be used in the calculation of fuzzy matrix and other defects, the fuzzy set theory is introduced into the traditional AHP, and will be consistent The concept of sex is transformed into the fuzzy matrix. It uses Flat or Trapezoidal Fuzzy Number and converts expert opinions to form a fuzzy upside-down matrix, uses Geometric Mean Method to calculate fuzzy weights, and then through hierarchical concatenation to calculate the value of each alternative Fuzzy weight, and finally use the membership function graph of each alternative fuzzy weight to arrange the priority of the scheme. The method is rigorous, but the disadvantage is that the calculation is too complicated. Buckley also pointed out that it is not easy to require decision makers to determine the value subjectively when comparing pairwise. When decision makers believe that the importance of iR is much more important than jR, is its importance ratio 7, 8, or 9? Such a choice may make decision makers feel quite troubled. However, setting a range of 7–9 for decision makers to choose from should reduce the burden on decision makers’ thinking. Therefore, Buckley introduces fuzzy numbers to show the fuzzy and uncertainty of decision-makers’ judgments. Mon, Cheng and Lin [6] Aimed at the problems of traditional AHP only in certain situations, the accuracy of decision-making can be more trustworthy, and the measurement scale is too subjective, etc., proposed the Entropy Weight Method as The basic Fuzzy AHP decision-making model is applied to the assessment of national defence weapon systems. Although this model is simple and easy to understand, each criterion’s weight is still subjectively determined by decision-makers, and group decision-making issues are not considered. Wu Yanhui [7] uses Fuzzy AHP and fuzzy comprehensive evaluation to conduct analysis and empirical investigations on semiconductor-related industry manufacturers, build a management ability evaluation weight system, and develop eight major evaluation indicators and 39 secondary evaluation indicators. Simultaneously, with the help of the weighting system and the influence of each evaluation index on management ability, a basis for simultaneous comparison and linkage is established to make a comprehensive evaluation feasible. Zhang Meijuan believes that the advantages of using FAHP include: it can handle research problems that are difficult to quantify: for example, the management strategy of the immature emerging industries, and the priority of resource allocation for social science. Reduce uncertainty when scholars and experts evaluate each element. It presents the vagueness of expert perception, and will not delete any individual opinions. Presenting the fuzzy interval in collective decision-making by experts can be used as a flexible space for decision-makers to adopt personal experience judgment. Use the fuzzy hierarchical analysis method to develop a decision-making model based on the minimum stockpile, and consider factors such as the type of original material, the place of production, the used space of the stockyard, the use of the equipment and the utilization rate of raw materials, and finally to test the fuzzy decision-making model, In order to explore the similarities and differences in the results produced by different methodologies such as fuzzy and crisp. Ou Junhong [8], Lee [9] uses FAHP to combine Benefits, Opportunities, Costs, and Risks (BOCR) to enable TFT-LCD manufacturers to consider BOCR when choosing their backlight component suppliers Next, and there is a complete selection mechanism. Since Yager first introduced Pythagorean fuzzy sets in 2014, Pythagorean fuzzy sets have been used in 115 many fields, especially in logistics, supplier selection, 116 facility locations, and risk assessment problems [10].

Through the multi-criteria decision-making method of fuzzy hierarchical analysis, the selection of notebook computer electronic components suppliers is discussed. The research results show that product quality factors are the essential key selection indicators when managers select suppliers, followed by cost /Process control, innovative research and development capabilities, and supplier management. According to the results of the research, it is hoped that when notebook computer OEMs select existing or new suppliers, they can understand the importance of various evaluation aspects and criteria, and make the most favourable decision for supplier selection [11], Some sars also use fuzzy triangular numbers in fuzzy numbers for research [12 –14].

This research aims to evaluate the influence of different factors in the pastoral complex through the fuzzy analytic hierarchy process, so as to design an excellent plan according to actual needs.

3 The core elements, structure, function and operation mechanism of fuzzy system of pastoral complex

Yan Chunqi et al. [15] discussed that “the structure of the ecosystem is the basic criterion for evaluating the quality of the system". The construction of the pastoral complex’s fuzzy computing system discusses modern agricultural upgrading and requires analysis of the policy environment and commercial market, and industrial implantation—planning and discussion of leisure product design and product consumption chain construction process.

3.1 The Core elements, structure and function of fuzzy system pastoral complex

The system structure of the pastoral complex refers to the sum of the interrelationships of the various components of the place and the agricultural and tourism integration system, the distribution of time and space, the transfer of matter and energy, etc., “a reasonable economic structure resides in an efficient ecosystem structure” [16]. The pastoral complex can include the following: “Scenic spots, scenic spots, infrastructure, reception facilities, and tourism practitioners, and their mutual configuration relationships in time and space” [17]. The rural revitalization strategy for building a rural complex has been implemented. We should attach great importance to and focus on solving the rural complex’s following problems: (1) Treat it as a project with a high level of commercialization, and ignore commercial planning and planning. There must be a qualified investment and operation entity as a prerequisite. If there is no qualified main body for operation, the government should not blindly establish projects and provide investment support. (2) Select the location of the project. Particular attention should be paid to the self-driving time for the target consumer group to reach the project location, within one hour of the city’s recreational circle. (3) Design of tourism products that are grounded and landable. The consumer industry chain’s design core is the product structure suitable for the consumer demand structure, and the consumption time chain is composed of travel routes and layout.

The pastoral complex can be regarded as a system construction process, and its development will inevitably progress gradually on the three levels of micro, meso, and macro. This is reflected in: at the micro-level, the pastoral complex focuses on land use. The environmental ecology and product creation of the complex is the micro-foundation of industrial symbiosis and the focal point of technological innovation in the pastoral complex, so the specific realization power of the system operation Including the implementation of cleaner production in products and production processes, so that resources can be recycled within the system, and resource utilization can be improved, especially in the pastoral complex tourism products to increase the proportion of cultural products; establishing a rural complex industry at the meso level A symbiotic real economy group, to establish an ecological industrial chain, so that resources can be recycled in the real economy of the park; to build a market-oriented regional industrial symbiosis chain network at the macro level, to form a regional agricultural tourism industry circular economy system evolving to a three-level ecosystem And sustainable development.

3.2 Construct the rural integrated system structure which is consistent with the rural resource structure and the commercial environment

In the context of industrial symbiosis, industrial informatization and other resources integration in the context of rural revitalization, clean production, ecological design, circular economy and other methods are organically combined with the rural elaborate system structure design plan, and the rural complex system model can be designed, as shown in the Fig. 1 shown. Among them, the ecological park is a high-level spatial distribution form of the pastoral complex, and the pastoral complex system is an advanced network system state of industrial symbiosis; the complex pastoral system is an area built on a regional scale that aims at the balanced development of economic and environmental benefits. It needs to pay attention to the improvement of the industrial environment, technological innovation, and multiple subjects’ joint participation. At present, the development of the pastoral complex system is facing three breakthrough methods: one is to build a rural complex ecological industry symbiosis model; the second is to adopt the ecological pastoral complex development model; the third is to build a model from the rural complex industrial system to the regional pastoral complex system. Industrial symbiosis includes “self-organization evolution, co-evolution, reasonable division of labour, cooperative competition mechanism, etc.” [18] The construction of rural complex industrial symbiosis mainly reflects “imitating the producers, consumers and decomposers in the natural ecosystem, and realizing energy circulation. “Mobile” ecological industrial chain construction, the industrial symbiosis of the pastoral complex is to design two or more agricultural and tourism integrated industrial chains to realize the integration and symbiosis of agriculture, tourism, handicraft manufacturing, research or cultural and creative industries to form a new type Innovation activities in the agricultural tourism system. Constructing the ecological industry chain of the pastoral complex can increase the stock of tourism resources, improve efficiency, meet more needs of the society, and realize the experience economy of cultural and technological innovation; the construction of the ecological industry chain of the pastoral complex is based on modern agriculture and experience economy. Concept innovation and the preservation of the ecological environment is the constraint; the construction of the pastoral complex ecological industry chain requires the coupling of more than two industries or units, and the technological innovation, operation innovation and management system innovation are organically integrated into a new composite system through the design of the industrial chain The specific manifestation of the industrial symbiosis model of the rural complex is also an advanced spatial distribution form of industrial symbiosis. The regional rural complex industrial symbiosis network that aims to balance economic and environmental benefits in the region needs to focus on technological innovation and the participation of multiple subjects.

Fig. 1

Conceptual model of the rural complex.

The pastoral complex uses ecological design, clean production and other means to achieve a closed-circuit material cycle and energy multi-level utilization mode by establishing multiple industrial chains, to form a symbiotic relationship between energy and information between the complex pastoral systems, thereby improving resource utilization efficiency And ecological efficiency, increase the degree of industrial relevance, form a horizontal and vertical coupling symbiotic relationship between industries, and significantly improve the economic and ecological performance of the pastoral complex.

3.3 Operating mechanism of rural integrated system under the strategic environment of rural revitalization

The operation process of constructing a regional pastoral complex system is essentially a process of human beings purposefully exploiting agricultural and rural ecological resources and environment, and realizing the rational allocation of production factors. The operation process of the pastoral complex system is an artificial ecosystem based on resource utilization, meeting human needs as the guide, and a social economic management system as the context. It has the characteristics of a multi-scale and multi-level system.

It can be seen from Fig. 2 that the systemic mechanism of the pastoral complex presents a complicated relationship. Socio-economic development is the driving force for the development of the pastoral complex. The construction of tourism facilities and tourism activities exert environmental pressure on the pastoral complex. The pastoral complex is social. The tourism products and the social management guarantee system that the environment meets the needs of human beings provide constraints and management functions for developing the pastoral complex.

Fig. 2

Schematic Diagram of the Function Mechanism of Rural Complex System in the Context of Rural Revitalization.

4 Fuzzy analytic hierarchy process

Professor L. A. Zadeh of the University of California, Berkeley, published a paper on “Fuzzy Set” in the “Information and Control” academic journal in 1965, and the fuzzy theory was born. Fuzzy theory can deal with inaccuracy and uncertainty and has been used as a modelling tool for a complex system [19]. Since the calculation result of FAHP is the final fuzzy score of each alternative plan, to determine which are the most suitable alternatives, the fuzzy number needs to be explained at last. Therefore, the fuzzy number is brought into the pairwise comparison, and then through the operation and reduction mode of the fuzzy number, the weight and ranking of each factor can be calculated for decision analysis. The fuzzy process (the ranking of the fuzzy number) needs to be adopted. The steps of the fuzzification process in this research are described as follows:

4.1 Establish a hierarchy

Establish a hierarchical framework for all relevant factors or factors.

4.2 Establishment of fuzzy positive inversion moment

In general, the comparison matrix was obtained through questionnaires completed by some experts in the field. We use Saaty [5] proposed evaluation transformation method. The design of an expert questionnaire is based on the concept of hierarchical analysis. Through the pairwise comparison of factors, the scale table of 1–9 evaluation is used, as shown in Table 1. Therefore, this study used tables to compare pairwise indicators at the same level to obtain expert opinions on the relative importance of the two factors.

Table 1
1–9 scale

Assessment scale Semantic wording Positive Fuzzy Number Reciprocal fuzzy number

1 Same important (1,1,1) (1,1,1)

2 Somewhere in between (1,2,3) (1/3,1/2,1)

3 Slightly important (2,3,4) (1/4,1/3,1/2)

4 Somewhere in between (3,4,5) (1/5,1/4,1/3)

5 Strong important (4,5,6) (1/6,1/5,1/4)

6 Somewhere in between (5,6,7) (1/7,1/6,1/5)

7 Extremely important (6,7,8) (1/8,1/7,1/6)

8 Somewhere in between (7,8,9) (1/9,1/8,1/7)

9 Absolutely important (9,9,9) (1/9,1/9,1/9)

Assessment scale	Semantic wording	Positive Fuzzy Number	Reciprocal fuzzy number
1	Same important	(1,1,1)	(1,1,1)
2	Somewhere in between	(1,2,3)	(1/3,1/2,1)
3	Slightly important	(2,3,4)	(1/4,1/3,1/2)
4	Somewhere in between	(3,4,5)	(1/5,1/4,1/3)
5	Strong important	(4,5,6)	(1/6,1/5,1/4)
6	Somewhere in between	(5,6,7)	(1/7,1/6,1/5)
7	Extremely important	(6,7,8)	(1/8,1/7,1/6)
8	Somewhere in between	(7,8,9)	(1/9,1/8,1/7)
9	Absolutely important	(9,9,9)	(1/9,1/9,1/9)

The fuzzy semantics of this study are represented using levels 1–9. As shown in Table 1, the semantic wording and triangular fuzzy number represented are displayed, and the opinions expressed by each expert are converted into fuzzy triangular numbers. The measurement scale can be divided into five levels 1, 3, 5, 7 and 9, respectively representing equal strength, slightly strong, quite strong, extremely strong and extremely strong. Between the five measurement scales, there are four levels of 2, 4, 6, and 8. The attribute function of the triangular fuzzy number is shown in Fig. 3.

Fig. 3

The attribute function of the triangular fuzzy number.

4.3 Integration of expert groups

Through formula (1), the geometric average method is used to integrate expert opinions. ${\tilde{A}}_{ij} = {({\tilde{A}}_{ij}^{1} \otimes {\tilde{A}}_{ij}^{2} \otimes {\tilde{A}}_{ij}^{3} \otimes \dots \otimes {\tilde{A}}_{ij}^{n})}^{\frac{1}{n}}$ (1)

${\tilde{A}}_{ij}$ : The triangular fuzzy number of the i column and the j row after integration.

${\tilde{A}}_{ij}^{n}$ : Expert n compares the relative importance of the j-th factor in the i-th evaluation index.

4.4 Calculate fuzzy weights

Because of the consistency problem in the AHP method, the geometric average method is used to calculate the fuzzy weight, in addition to the calculation of the triangular fuzzy up-and-down value matrix, at the same time, this can achieve the purpose of normalization. $R_{i} = {({\tilde{A}}_{i 1} \otimes {\tilde{A}}_{i 2} \otimes {\tilde{A}}_{i 3} \otimes \dots \otimes {\tilde{A}}_{in})}^{\frac{1}{n}}$ (2) ${\tilde{W}}_{i} = R_{i} \otimes {(R_{1} \oplus R_{2} \oplus R_{3} \oplus \dots \oplus R_{n})}^{- 1}$ (3)

r_i: Geometric average mean of triangular fuzzy numbers.

${\tilde{W}}_{i}$ : Fuzzy weight values for each column in the fuzzy positive inversion matrix.

4.5 Defuzzification

Through the formula, the defuzzification value of each item is obtained. Because the weight of the evaluation item is a fuzzy value, it must be converted into a non-fuzzy value through the defuzzification method to achieve objectivity without adding experts’ preferences. ${Df}_{ij} = \frac{l + m + u}{3}$ (4)

l,m,u represent the lower limit, middle and upper limit of triangular fuzzy number respectively.

4.6 Normalized weights

In order to compare the importance of different main aspects and various evaluation indicators, the defuzzification weight value is normalized. ${NW}_{i} = \frac{{Df}_{ij}}{\sum {Df}_{ij}}$ (5)

NW_i: Normalized weights.

4.7 Consistency check

In order to test whether the paired comparison matrix is consistent before and after the comparison, the consistency test is performed. It is necessary to calculate the Consistence Index (CI) to ensure that the matrix is more in line with the actual situation. As long as the Aij value in the reciprocal matrix is changed, λ max will change. Therefore, the degree of difference between λmax and n calculated by formula (6) can be used as a criterion for judging the consistency index. $CI = \frac{λ max - n}{n - 1}$ (6)

According to Saaty recommended CI $\underline{\underline{<}}$ 0.1, it is the acceptable deviation range. Random index (Random index: RI) is a consistency index. As shown in Table 2, it is a matrix of upside and down values generated from evaluation scales 1 to 9. The RI value increases as the order of the matrix increases. n and its relative random index RI, the ratio of CI value to RI value under the same order matrix is called the consistency ratio CR (Consistence Ratio), calculated by the formula (7), if CR $\underline{\underline{<}}$ 0.1, then consistency Reach the acceptable level.

Table 2

Random index (RI) table

n	1	1	3	4	5	6	7	8
RI	0	0	0.58	0.90	1.12	1.24	1.32	1.41

5 Result and discussion

In this study, the questionnaires’ results were combined with the use of fuzzy analytic hierarchy process (FAHP) to obtain expert scores about the relative importance of each factor, and a pairwise comparison matrix was established, and fuzzy triangular numbers were used to integrate and evaluate expert opinions. According to the FAHP method proposed by Buckley, the triangular fuzzy number is calculated, and the data is deblurred and normalized weight to obtain the relative weight value of each evaluation index. In this study, all the survey data collected are entered into Excel tables. According to the steps of the FAHP method, various evaluation indicators are calculated in turn. The actual calculation example is as follows:

5.1 Build a pairwise comparison matrix

Fill out the questionnaire according to the experts, the results are shown in Table 3, and the matrix is blurred, as shown in Table 4.

Table 3
Comparison matrix in pairs

Core elements Structure Function Operating mechanism

Core elements 1 2 3 1/2

Structure 1/2 1 4 1/3

Function 1/3 1/4 1 1/3

Operating mechanism 2 3 3 1

	Core elements	Structure	Function	Operating mechanism
Core elements	1	2	3	1/2
Structure	1/2	1	4	1/3
Function	1/3	1/4	1	1/3
Operating mechanism	2	3	3	1

Table 4

Fuzzy pairwise comparison matrix

	Core elements	Structure	Function	Operating mechanism
Core elements	(1,1,1)	(1,2,3)	(2,3,4)	(1/3,1/2,1)
Structure	(1/3,1/2,1)	(1,1,1)	(3,4,5)	(1/4,1/3,1/2)
Function	(1/4,1/3,1/2)	(1/5,1/4,1/3)	(1,1,1)	(1/4,1/3,1/2)
Operating mechanism	(1,2,3)	(2,3,4)	(2,3,4)	(1,1,1)

5.2 Weight calculation

Use formula (2) to calculate the geometric mean of fuzzy triangular numbers. As shown in Table 5, follow the FAHP steps. Through procedure (3), the fuzzy weight values of each evaluation index can be obtained. Finally, the fuzzy weight values obtained are subjected to the formula (4) Perform defuzzification and finally normalize by formula (5) to obtain the evaluation criterion’s relative weight value. The calculated values are shown in Table 6. It can be seen from Fig. 4 that the proportions of the four evaluation criteria in this case are not the same, and their preference levels from large to small are the operating mechanism, core elements, structure, and function. Among them, the operating mechanism is particularly emphasized. Elements and design have lower parts.

Table 5
Geometric mean of triangular fuzzy numbers

Geometric mean of triangular fuzzy numbers

Core elements 0.90 1.32 1.86

Structure 0.71 0.90 1.26

Function 0.33 0.41 0.54

Operating mechanism 1.41 2.06 2.63

	Geometric mean of triangular fuzzy numbers
Core elements	0.90	1.32	1.86
Structure	0.71	0.90	1.26
Function	0.33	0.41	0.54
Operating mechanism	1.41	2.06	2.63

Table 6

Weight table

Evaluation criterion	Fuzzy Weight Value			Defuzzification weight	Normalized weights
Core elements	0.14	0.28	0.55	0.33	0.29
Structure	0.11	0.19	0.37	0.23	0.20
Function	0.05	0.09	0.16	0.10	0.09
Operating mechanism	0.22	0.44	0.78	0.48	0.43

Fig. 4

Evaluation criteria weight ranking.

5.3 Consistency check

According to the calculation, the maximum characteristic value λmax = 4.222 is obtained. According to formula (6) and formula (7), the value of the consistency index and the consistency ratio are obtained to determine that the questionnaire result is logical, as shown in Table 7.

Table 7
Weight table

Evaluation criteria Core elements Structure Function Operating mechanism

Normalized weights 0.29 0.20 0.09 0.43

λ max= 4.222, CI = 0.073, CR = 0.082

Evaluation criteria	Core elements	Structure	Function	Operating mechanism
Normalized weights	0.29	0.20	0.09	0.43
λ max= 4.222, CI = 0.073, CR = 0.082

Through the level as mentioned above consistency test, the consistency index values and the consistency ratio are less than 0.1. Therefore, this study’s questionnaire results are consistent, that is, conform to the expert opinion consistency test.

6 Conclusions

The pastoral complex can be regarded as an effective carrier for the implementation of the rural revitalization strategy, with modern agriculture integrated with modern service industry as the core industry, focusing on the cultural and creative sectors and ecological agriculture of agricultural tourism, cultural tourism, and increasing investment and intelligence promotion. Strengthen several leading ecological pastoral complexes, cultivate several guiding ecclesiastical complexes, and reserve several environmental, industrial park construction projects involving agricultural tourism. Carry out the technological innovation and management upgrade of the pastoral complex, support the research and development, application of ecological agriculture technology and environmental supply chain management, implement green technology and environmental engineering, and create a group of modern agricultural industries with demonstration effects and promotion significance for the integration of agriculture and tourism The park is based on modern agriculture, supported by modern service industries, and integrated with the industrial environment, policies and regulations, and the comprehensive upgrade of technology applications, to improve the direct economic benefits obtained from the operation of agricultural tourism and realize the integration of the traditional agricultural system to the rural tourism. A modern agricultural and tourism integrated industrial system developed by the complex.

This study uses the Fuzzy Analytic Hierarchy Process (FAHP) is a multi-criteria decision-making (MCDM) method to develop a selection tool that quantifies and ranks four critical pastoral complex project criteria according to importance. Prioritizing these criteria according to their relative importance, helps decision-makers identify more important project elements that require additional attention, and improve the selection process when evaluating different alternatives for pastoral complexes, so as to design an excellent program to meet actual needs.

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