# Construction and Index Analysis of Whole Chain Linkage Talent Training System Based on Fuzzy AHP Model

Authors: Liu Yi; Sun Yan
Journal: Journal of Sensors
 (2022)

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## Abstract

The construction and analysis of the entire chain linkage talent training system can measure the level of education and at the same time improve talents and drive development for the society. This paper uses the AHP model and CPII model to analyze the construction and index analysis of the entire chain linkage talent training system. The research shows that the weight value of personality literacy is the largest, followed by innovation literacy, emotional intelligence literacy, leadership and management ability literacy, and scientific literacy; in the analysis of the importance of the first-level indicators, it is found that most people think that personality literacy is the most important; in the consistency test of the first-level indicators, it shows that the results have good consistency; in the comment set of the second-level indicators, the highest comment value is expertise, independence and practicality, and diligence. Through the evaluation of the talent training system and its operation quality, it can provide reform ideas for education and management departments and can fully play the role of talents, create a good working atmosphere, and create suitable ways to improve capabilities. Through AHP’s research on the construction of the entire chain linkage talent training system and indicators, it is found that the personality literacy has the largest weight value among the five first-level indicators. The cultivation of leadership, management ability, scientific literacy, etc. can better reflect the characteristics of talents.


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## Body

## 1. Introduction

According to the needs of enterprises, the article forms a quality evaluation system of ability training from three aspects: basic ability, work ability, and expansion ability, which consists of 15 sub-indicators. The evaluation results are analyzed by data envelopment analysis (DEA), fuzzy comprehensive evaluation (Fuzzy), and analytic hierarchy process (AHP), which helps to optimize the talent training program [1]. In the process of determining the classification system of key stakeholders based on the enterprise stakeholder theory, this paper uses the analytic hierarchy process to determine the weights of the relevant attributes of different stakeholders when using the stakeholder theory to determine the classification system of key stakeholders. Are synthesized and ordered using the Analytical Hierarchy Process. At the same time, the article uses the expert selection decision support package to conduct an overall evaluation of the company’s stakeholder classification system and combines case studies and innovative thinking to study the research methods of stakeholder theory in theoretical research and practice. [2]. Establishing an evaluation system of scientific talent training mode that meets the needs of professional talents is of great significance for promoting the development of tourism English in colleges and universities and the social needs of cultivating high-skilled talents. Using AHP, firstly, establish a hierarchical analysis indicator system structure model and describe each index; then, establish a mathematical model, including two steps of judgment matrix and hierarchical single ranking; finally, calculate the weight of the indicator system according to the solution steps. When developing the training model, the training of key indicators and other nonmain indicators should be strengthened [3]. This paper proposes Analytical Hierarchy Process (AHP) as a potential safety management assessment method. Few safety performance assessment models can reflect the dynamic performance of construction project sites, so take the problem of building dynamic models as an example. According to the input content, the construction projects are arranged in descending order, so that the weak links of the safety performance of the construction projects can also be judged, and it is hoped that the application of safety management professionals can be encouraged to evaluate the AHP [4]. This paper analyzes the new requirements for sports media practitioners in the new media era from the aspects of personal quality, professional quality, and professional skills. This paper discusses the basic idea of talent education system evaluation through the process of analyzing levels and vague general evaluation methods [5]. In this paper, the construction of the evaluation set of the index system, the determination of the index weight, the solution of the fuzzy evaluation matrix, the calculation of the comprehensive evaluation vector, and the establishment of the evaluation model. Through the evaluation of the talent training system and its operation quality, it provides reform ideas for teaching [6]. The AHP fuzzy evaluation method is to design quantitative indicators for qualitative decision-making problems and establish a quality evaluation model based on the evaluation criteria and the setting of factor weights. This article discusses the feasibility and effectiveness of the indicators of the talent training quality evaluation system by establishing an AHP model and improves the work efficiency of talents [7]. With the advent of the information age, the demand for computer professionals such as network maintenance engineers, security engineers, and development and operation and maintenance engineers has gradually increased, and computer software has become one of the most popular industries at present and in the future. This paper uses the Analytic Hierarchy Process (AHP) to analyze the established evaluation index system of software technology majors in higher vocational colleges and explores effective strategies to strengthen the application of high-efficiency personnel training mode for software technology majors in higher vocational colleges [8]. In view of the society’s demand for talents, this paper adopts the analysis level process to conduct qualitative and quantitative analysis and decision-making and draws the conclusion that the proportion of comprehensive personnel training under the overall goal of G is 41.867%, and the proportion of research-oriented personnel training under the overall goal of G is 42.1456% %, and the proportion of skilled personnel training is 15.9874% G’s overall goal [9]. This paper discusses the basic idea of ​​talent education system evaluation through the process of analyzing levels and vague general evaluation methods and establishes an index system. This paper studies the application of the Analytic Hierarchy Process (AHP) model in the evaluation of college students’ educational quality, promotes the basic principles of the quality evaluation of students’ talent training, and designs an evaluation index system. And pointed out that in order to improve the quality of college students, we must focus on training programs, training measures, and quality evaluation in the order of importance [10]. Establishing an evaluation system of scientific talent training mode that meets the needs of professional talents is of great significance for promoting the development of tourism English in colleges and universities and the social needs of cultivating high-skilled talents. The result of using AHP analysis is that colleges and universities should strengthen the training of key indicators and other nonmain indicators when formulating training models [11]. Through the exploration front-line talent training project, the evaluation index system of the geological exploration front-line talent training project was creatively established from the three dimensions of input index, process index, and output index, and the weight of each index in the system was determined by the analytic hierarchy process; research and establishment of geological exploration Comprehensive analysis model of front-line talent training, evaluation methods and evaluation standards [12]. This paper clarifies the object of international economic and trade professional personnel training, uses the analytic hierarchy process to build a talent quality system, and puts forward suggestions for the optimization of the international economic and trade professional personnel training system [13]. This paper discusses how to construct a regional scientific and technological innovation evaluation system based on AHP and calculates the index weight of the evaluation system, aiming to provide a reference for the decision-making and formulation of relevant policies. By constructing such an evaluation system, it will play a role in the technological innovation in Jiuquan area [14]. Guided by talent theory, system theory, and education quality theory, this paper analyzes the current situation of domestic credit management talent training based on talent quality theory and literature analysis, and uses brainstorming and Delphi methods to build an innovative and high-quality structural framework, and employs a hierarchical process of analysis to determine the weighting of quality elements to provide benchmarks for decision-making to improve creative excellence [15].


## 2. Construction of the Whole Chain Linkage Personnel Training System

### 2.1. The Connotation of the Whole Chain Linkage Personnel Training System

Due to the lack of the highest-level education system design, China’s current talent education system is basically a fragmented point-like structure, but in reality it is superficial, estranged, and at different speeds. In this way, the core points of each link in the talent training system can form a complementary and progressive “chain”, making the “whole chain” a reality. Linked personnel training system construction has formed an institutional environment and a good atmosphere that encourages the healthy growth and development of professional and skilled personnel training and ensures the sustainable and healthy development of professional and skilled personnel training.


### 2.2. The Value of the Whole Chain Linkage Personnel Training System

With the gradual development of this concept, the concept of the chain connecting the entire human education has gradually extended to all aspects of society. The process of its integration into talent education is to link school education, talent development, and social monitoring. Entrepreneurship service aims to ensure the controllable inheritance of human resources in the stage of social development, which is consistent with the purpose of building a domestic higher education talent training system and strengthening social practice research throughout my country’s talent training goals. At the same time, there are problems such as unbalanced resource allocation and weak market awareness in the current process of domestic scientific and technological innovation and entrepreneurial talent training. The above two aspects are the theoretical induction and analysis of the construction of the entire talent training chain system.


### 2.3. Build a “Whole Chain” Linkage Talent Training System

First, the talent incubation system, the focus of “whole chain” talent training is to provide long-term high-quality human resources for the society, so the talent training system can establish a special talent training institution, which is mainly responsible for the formulation of human resources planning and talent training planning, the implementation of the annual talent training plan, the evaluation of the training effect and other related matters, or cooperation with colleges and universities to specially train the required technical talents, so that the talent training can be integrated into the whole process, and give full play to the role of teaching, production, and scientific research in improving students’ practical skills and professional skills. The function of collaborative development; it can also play the role of industry organizations in talent training. Second, the talent allocation system, after completing the talent training, it is necessary to establish a talent allocation system to allocate talents scientifically and reasonably. Such an approach can maximize the benefits of talent investment and is necessary to promote development. Under such a background, different career development plans should be established for different talents according to their situation, so that talents can work and study according to the corresponding plans. Promote talents to continuously improve their comprehensive ability, so as to realize the comprehensive development of society. Third, the peripheral support system for talents, which is also a very critical part of the talent training process. In the peripheral support system, the most important position is financial support. Due to the long period of talent training, it is necessary to have sufficient funds as backup support in the process of talent training, and special funds can be set up as reserve funds for talent training.


### 2.4. The Importance of the Whole Chain Linked Person Training System

The establishment of a whole chain talent education system is conducive to building an excellent team that can adapt to the design and development strategy, and build a group of outstanding talents with leadership, pioneering and innovative, institutional innovation, technological innovation, and sharp vitality for my country’s economic development. Strengthening of social quality and talent education level. Fully understand the importance of talents, cultivate talents, establish a healthy talent guarantee mechanism, improve the talent incentive system, effectively guarantee the flow of talents, give full play to the maximum role of talents, create a good working environment, and create a suitable environment for talents to cultivate talents.


## 2.1. The Connotation of the Whole Chain Linkage Personnel Training System

Due to the lack of the highest-level education system design, China’s current talent education system is basically a fragmented point-like structure, but in reality it is superficial, estranged, and at different speeds. In this way, the core points of each link in the talent training system can form a complementary and progressive “chain”, making the “whole chain” a reality. Linked personnel training system construction has formed an institutional environment and a good atmosphere that encourages the healthy growth and development of professional and skilled personnel training and ensures the sustainable and healthy development of professional and skilled personnel training.


## 2.2. The Value of the Whole Chain Linkage Personnel Training System

With the gradual development of this concept, the concept of the chain connecting the entire human education has gradually extended to all aspects of society. The process of its integration into talent education is to link school education, talent development, and social monitoring. Entrepreneurship service aims to ensure the controllable inheritance of human resources in the stage of social development, which is consistent with the purpose of building a domestic higher education talent training system and strengthening social practice research throughout my country’s talent training goals. At the same time, there are problems such as unbalanced resource allocation and weak market awareness in the current process of domestic scientific and technological innovation and entrepreneurial talent training. The above two aspects are the theoretical induction and analysis of the construction of the entire talent training chain system.


## 2.3. Build a “Whole Chain” Linkage Talent Training System

First, the talent incubation system, the focus of “whole chain” talent training is to provide long-term high-quality human resources for the society, so the talent training system can establish a special talent training institution, which is mainly responsible for the formulation of human resources planning and talent training planning, the implementation of the annual talent training plan, the evaluation of the training effect and other related matters, or cooperation with colleges and universities to specially train the required technical talents, so that the talent training can be integrated into the whole process, and give full play to the role of teaching, production, and scientific research in improving students’ practical skills and professional skills. The function of collaborative development; it can also play the role of industry organizations in talent training. Second, the talent allocation system, after completing the talent training, it is necessary to establish a talent allocation system to allocate talents scientifically and reasonably. Such an approach can maximize the benefits of talent investment and is necessary to promote development. Under such a background, different career development plans should be established for different talents according to their situation, so that talents can work and study according to the corresponding plans. Promote talents to continuously improve their comprehensive ability, so as to realize the comprehensive development of society. Third, the peripheral support system for talents, which is also a very critical part of the talent training process. In the peripheral support system, the most important position is financial support. Due to the long period of talent training, it is necessary to have sufficient funds as backup support in the process of talent training, and special funds can be set up as reserve funds for talent training.


## 2.4. The Importance of the Whole Chain Linked Person Training System

The establishment of a whole chain talent education system is conducive to building an excellent team that can adapt to the design and development strategy, and build a group of outstanding talents with leadership, pioneering and innovative, institutional innovation, technological innovation, and sharp vitality for my country’s economic development. Strengthening of social quality and talent education level. Fully understand the importance of talents, cultivate talents, establish a healthy talent guarantee mechanism, improve the talent incentive system, effectively guarantee the flow of talents, give full play to the maximum role of talents, create a good working environment, and create a suitable environment for talents to cultivate talents.


## 3. Fuzzy AHP Model

### 3.1. Establishment of Hierarchical Hierarchy

#### 3.1.1. Hierarchical Structure and Composition

The AHP process begins by layering the decision problem. The so-called hierarchical structure divides the problem into different components according to the nature of the problem and the goal to be achieved and groups them in a nonuniform layer according to the degree and degree of correlation between the factors. AHP first divides the level into different levels. The top layer is called the paint layer; this layer has only one element, that is, the problem must achieve the goal or desired result, the middle layer is the standard layer, and the element of this layer is the dimension. Approved policies, guidelines, etc. to achieve goals. The standard layer can have multiple layers, which can be divided into standard layers and substandard layers according to the size and complexity of the problem, and the lowest layer is the model layer, with options to achieve the goal. In a hierarchy, each level consists of several factors. When a hierarchy contains many factors, the hierarchy can be further subdivided into several sublevels. Generally speaking, the number of elements controlled by each factor at each level should generally not exceed 9, because too many control elements will cause pairwise comparisons to be difficult, as shown in Figure1.Figure 1
Typical hierarchical hierarchy.Generally, no more than 9 objects are compared under one criterion, because psychologists believe that making pairwise comparisons is too much beyond human judgment. At most, it is roughly between 5 and 9. If it is limited to 9, it is appropriate to use a 1-9 scale to express the difference between them.


### 3.2. Constructing the Comparison Judgment Matrix

Once the hierarchy is established, the connections between the top and bottom elements are determined. Assuming that the element C of the previous layer is a criterion, and the proportion of the next dominant layer isu1,u2,⋯,un, our purpose is to give the corresponding weights u1,u2,⋯,un according to their effect on C. The relative importance of is given corresponding weights. Some questions can be directly weighted, such as student test scores and project investment amounts but in most socioeconomic activities, especially in more complex questions, the weights of elements cannot be directly obtained, which requires appropriate methods to derive their weights. The method used by AHP to deduce the weight is the pairwise comparison method, which compares the characteristics of the judgment matrix:
(1)
aij>0(2)
aij=1/aji(3)
aii=1i,j=1,2,3,⋯nThe judgment matrixA is
(1)A=1a12⋯a1n1/a121⋯a2n⋯⋯⋯⋯1/a1n1/a2n⋯1.A matrix of ordern with the above three characteristics is called a positive and negative matrix.All elementsaij must be transitive, i.e., aij the equation needs to be satisfied:
(2)aijajk=aiki,j,k=1,2,⋯,n.If then-order matrix A is a positive and inverse matrix, it has for all i, j, and kaijajk=aiki,j,k=1,2,⋯,n, then A is called a consistent matrix.According to the reference estimation matrixA, when comparing n factors, only nn−1/2 pairwise comparisons are required. But the consistency A of the outer matrix of order nn−1/2 must be satisfied. Comparing all the same is too strict, so in practice, we do not require the equation matrix A to be consistent.When comparingn factors with a certain factor, some people think that it only needs to do n−1 times. The disadvantage of this approach is that any error in judgment may lead to unreasonable sorting. For systems that are difficult to quantify, errors in judgment should be avoided as much as possible. Doing nn−1/2 pairwise comparisons can provide more information and compare them from different angles to get a reasonable ordering.


### 3.3. Sorting and Consistency Test under Single Criterion

#### 3.3.1. Sorting under a Single Criterion

The AHP database is a comparative assessment matrix. Because each criterion controls multiple factors at the next level, a comparative evaluation matrix of each criterion and the factors it controls can be obtained. Therefore, the process of calculating the relative order weights of each factoru1,u2,⋯,un according to the reference matrix is called sorting according to a criterion. There are many calculation methods for the weights w1,w2,⋯,wn, among which the signature root method is a relatively mature and widely used method in the AHP, which is of great significance to the theoretical and practical development of the AHP.


#### 3.3.2. Method for Finding Positive and Negative Matrix Sorting Vector

For positive matrices, there is a simple algorithm (power method) to find the eigenvectors. The following theorem provides the theoretical basis for the power method. Letn be a matrix where V is the eigenvector corresponding to the largest eigenvalue of A and c is a constant. If x=e (e is a unit vector), then W is the normalized eigenvector corresponding to the largest eigenvalue of A, hereinafter referred to as the weight vector or the sorting vector.In the first step, normalize the column items of the judgment matrix:(3)A∼ij=aij∑i=1naij.In the second step, theA∼ij by line:
(4)w∼=∑j=1na1j∑i=1naij,∑j=1na2j∑i=1naij,⋯∑j=1nanj∑i=1naij,T.In the third step, thew∼ after normalization:
(5)W=ω1,ω2,⋯ωnT.In the fourth step,λ is the largest eigenvalue of A:
(6)λ=1n∑i=1nAWiωi.In the first step, normalize the column vector of the judgment matrix.In the second step, theAij∼ by row:
(7)W¯=∏j=1na1j∑i=1naij1/n,∏j=1na2j∑i=1naij1/n,⋯,∏j=1nanj∑i=1naij1/nT.In the third step, thew∼ after normalization:
(8)W=ω1,ω2,⋯ωnT.In the fourth step,λ is the largest eigenvalue of A:
(9)λ=1n∑i=1nAWiωi.


#### 3.3.3. Consistency Check

The complexity of objective things makes our judgments subjective and one-sided, so that every comparison and judgment cannot require exactly the same standard of thinking. Therefore, we do not require allnn+1/2 equations to be consistent when constructing the base matrix. However, it may also be that A and B are more important, Bis more important than C, and Cis more important than A, and this comparison is very inconsistent. When we compare rating matrices, we do not need to require ratings to be consistent. But a confusing and untenable comparative evaluation matrix can lead to wrong decisions, so we want the evaluations to be generally consistent. The above method for calculating weights is questionable when the estimated matrix deviates too much from the consistency. Therefore, when sorting each level by one criterion, it is necessary to check for consistency. Let A be a positive and inverse matrix of order n, we know from the theorem.
(10)AW=λmaxW,andλmax≥n.Likeλmax is much larger than n, then the degree of inconsistency of A is such that
(11)CI=λmax−nn−1.Inλmax, the largest eigenvalue is A, and CI can be used as a quantitative standard to measure the degree of inconsistency, which is called the consistency index. When CR<0.1, the consistency of the reference matrix is considered acceptable; otherwise, the evaluation matrix must be checked accordingly.TheCR value is less than 0.1, which meets the judgment requirements, indicating that the results have good consistency, and vice versa.


### 3.4. Hierarchical Total Sorting

Computing the relative importance scale (also known as the ranking weight vector) of all elements of the same level to the highest level (overall target) is called the overall ranking of the level.


#### 3.4.1. Steps of Hierarchical Total Sorting

(1)
Calculate the relative weight from the weight vector of all factors in the same layer to the highest layer; this process is carried out layer by layer from top to bottomSuppose that by calculating thek-th layer, there are k−1 layers with nk−1 a vector of sorting weights obtained by elements of elements relative to the total objects
(12)ωk−1=ω1k−1,ω2k−1,⋯,ωnk−1k−1TLayerKnk elements, they depend on some factors from the previous layer (k−1 layer) ui the full vector for single criterion sort is:
(13)pik=ω1ik,ω2ik,⋯,ωnkikT.For the correspondence with no dominance relation to thei-th element of the k−1 layer, uij value is 0.
(2)
Thek-th layer nk the sorting weight vector of elements relative to the total target is:(14)ω1k,ω2k⋯,ωnkkT=p1k,p2k,⋯,pk−1kωk−1


#### 3.4.2. Total Ranking Consistency Check

When one compares the elements of each level, even if each level uses essentially the same benchmark, there may still be differences between the levels accumulated in the overall ranking of the levels calculated step-by-step, and it is necessary to test whether the accumulation of this difference scale has any effect on the overall model. Significantly, the testing process is called Hierarchical Universal Classification Conformance Testing.Assuming that thej-th factor of the (k−1-th layer is the reference standard, the first-level and first-level consistency indicators for the pairwise comparison of each factor in the k-th layer are:
(15)ωk−1representsthetotalrankingvectorofthetotaltargetofthek−1layerRIk=RIk−1·ωk−1,CRk=CRk−1+CIkRIk3≤k≤n.IfCRk<0.1, it can be considered that the evaluation model has achieved local satisfaction at the k-layer level.


### 3.5. Adjustment of Judgment Matrix

When a comparative judgment matrix deviates too much from consistency, its reliability is questionable, and the judgment matrix must be adjusted at this time. In practical applications, the judgment matrix needs to be adjusted many times before it can pass the consistency test. At present, there are many ways to modify the judgment matrix, which can be roughly divided into three categories:(i)
Experience adjustment method: Let experts readjust some elements of the judgment matrix. This kind of method has a certain degree of subjective arbitrariness and lacks theoretical scientific basis(ii)
Construct a completely consistent judgment matrix with a certain method and extract the information of the original judgment matrix and the completely consistent matrix through the formaldehyde method, so as to achieve the purpose of adjustment. Such methods have certain blindness(iii)
Using the relationship between changes in matrix elements and consistency, identify key elements that affect consistency and make adjustments. Such methods change less elements of the original judgment matrix and retain more original informationThe following is the third type of evaluation matrix adjustment method, which is called the forward-looking algorithm of adjusting the AHP to evaluate the consistency of the matrix. The specific algorithm is as follows:Construct the matrix: Determine the elements of matrixA, aij used is replaced by aikakj, and aik use 1/aikakj, the matrix obtained after substitution, namely, Aijk=aijk.
(16)astk=askakts=i,t=j1/askakts=j,t=iastother.Becauseaskakt may be greater than 9, 1/askakt may be less than 1/9. This is inconsistent with the definition of judging oranges and may need to be fine-tuned, as follows:
(17)astk=Faskakts=i,t=j1/Faskakts=j,t=iastother.In,(18)Fx=9x≥9x1≤x<911/x1/9≤x<11/90<x<1/9.CalculationΔijk and Δij:
(19)Δijk=C.RA−C.RAijk,k≠i,j;k∈N.There aren−2 improvement degrees, such as Δijk=C.RA−C.RAijk≤0. It shows that the k-th adjustment does not help or even hinders the consistency of the judgment matrix, so it is set to 0 in the algorithm display.Δij is the element in the judgment matrix Aaij the n-2 maximum possible improvements. LikeΔij=0, all adjustment directions of aij do not help to improve the consistency of the judgment matrix.
(20)Δij=maxk∈Nk≠i,jC.RA−C.RAijk.CalculationTijTij is the adjustment strategy number corresponding to the maximum possible improvement and Dij degree of aij to moment consistency. If Dij =0, it means that all the adjustment directions of aij do not help to improve the consistency of the matrix.
(21)Tij=argmaxk∈Nk≠i,jC.RA−C.RAijk.(1) Adjust the empirical method. Let experts readjust some elements of the judgment matrix, but this method does have a theoretical basis. (2) Construct a consistent judgment matrix by the method, and extract the information of the original and consistent matrix by the formaldehyde method, and achieve the purpose of adjustment all the time. (3) Using the relationship between matrix element changes and consistency, determine and adjust the elements that affect consistency.


### 3.6. Calculation of Evaluation Set

Establishing an index set according to the evaluation index systemX=x1,x2⋯xmTand weight setw=w1,w2,⋯wm, the relationship between the evaluation index set Y, the factor set, and the weight set is:
(22)Y=Wn1TX=∑i=1mwixi,where Y is the final evaluation value of talent training.(1) Establish a hierarchical structure of the system. (2) Construct a pairwise comparison judgment matrix. (3) Calculate the sorting weight vector of the next level to a certain criterion of the previous level. (4) Total sorting is to calculate the sorting weight vector of each scheme to the total system target.


## 3.1. Establishment of Hierarchical Hierarchy

### 3.1.1. Hierarchical Structure and Composition

The AHP process begins by layering the decision problem. The so-called hierarchical structure divides the problem into different components according to the nature of the problem and the goal to be achieved and groups them in a nonuniform layer according to the degree and degree of correlation between the factors. AHP first divides the level into different levels. The top layer is called the paint layer; this layer has only one element, that is, the problem must achieve the goal or desired result, the middle layer is the standard layer, and the element of this layer is the dimension. Approved policies, guidelines, etc. to achieve goals. The standard layer can have multiple layers, which can be divided into standard layers and substandard layers according to the size and complexity of the problem, and the lowest layer is the model layer, with options to achieve the goal. In a hierarchy, each level consists of several factors. When a hierarchy contains many factors, the hierarchy can be further subdivided into several sublevels. Generally speaking, the number of elements controlled by each factor at each level should generally not exceed 9, because too many control elements will cause pairwise comparisons to be difficult, as shown in Figure1.Figure 1
Typical hierarchical hierarchy.Generally, no more than 9 objects are compared under one criterion, because psychologists believe that making pairwise comparisons is too much beyond human judgment. At most, it is roughly between 5 and 9. If it is limited to 9, it is appropriate to use a 1-9 scale to express the difference between them.


## 3.1.1. Hierarchical Structure and Composition

The AHP process begins by layering the decision problem. The so-called hierarchical structure divides the problem into different components according to the nature of the problem and the goal to be achieved and groups them in a nonuniform layer according to the degree and degree of correlation between the factors. AHP first divides the level into different levels. The top layer is called the paint layer; this layer has only one element, that is, the problem must achieve the goal or desired result, the middle layer is the standard layer, and the element of this layer is the dimension. Approved policies, guidelines, etc. to achieve goals. The standard layer can have multiple layers, which can be divided into standard layers and substandard layers according to the size and complexity of the problem, and the lowest layer is the model layer, with options to achieve the goal. In a hierarchy, each level consists of several factors. When a hierarchy contains many factors, the hierarchy can be further subdivided into several sublevels. Generally speaking, the number of elements controlled by each factor at each level should generally not exceed 9, because too many control elements will cause pairwise comparisons to be difficult, as shown in Figure1.Figure 1
Typical hierarchical hierarchy.Generally, no more than 9 objects are compared under one criterion, because psychologists believe that making pairwise comparisons is too much beyond human judgment. At most, it is roughly between 5 and 9. If it is limited to 9, it is appropriate to use a 1-9 scale to express the difference between them.


## 3.2. Constructing the Comparison Judgment Matrix

Once the hierarchy is established, the connections between the top and bottom elements are determined. Assuming that the element C of the previous layer is a criterion, and the proportion of the next dominant layer isu1,u2,⋯,un, our purpose is to give the corresponding weights u1,u2,⋯,un according to their effect on C. The relative importance of is given corresponding weights. Some questions can be directly weighted, such as student test scores and project investment amounts but in most socioeconomic activities, especially in more complex questions, the weights of elements cannot be directly obtained, which requires appropriate methods to derive their weights. The method used by AHP to deduce the weight is the pairwise comparison method, which compares the characteristics of the judgment matrix:
(1)
aij>0(2)
aij=1/aji(3)
aii=1i,j=1,2,3,⋯nThe judgment matrixA is
(1)A=1a12⋯a1n1/a121⋯a2n⋯⋯⋯⋯1/a1n1/a2n⋯1.A matrix of ordern with the above three characteristics is called a positive and negative matrix.All elementsaij must be transitive, i.e., aij the equation needs to be satisfied:
(2)aijajk=aiki,j,k=1,2,⋯,n.If then-order matrix A is a positive and inverse matrix, it has for all i, j, and kaijajk=aiki,j,k=1,2,⋯,n, then A is called a consistent matrix.According to the reference estimation matrixA, when comparing n factors, only nn−1/2 pairwise comparisons are required. But the consistency A of the outer matrix of order nn−1/2 must be satisfied. Comparing all the same is too strict, so in practice, we do not require the equation matrix A to be consistent.When comparingn factors with a certain factor, some people think that it only needs to do n−1 times. The disadvantage of this approach is that any error in judgment may lead to unreasonable sorting. For systems that are difficult to quantify, errors in judgment should be avoided as much as possible. Doing nn−1/2 pairwise comparisons can provide more information and compare them from different angles to get a reasonable ordering.


## 3.3. Sorting and Consistency Test under Single Criterion

### 3.3.1. Sorting under a Single Criterion

The AHP database is a comparative assessment matrix. Because each criterion controls multiple factors at the next level, a comparative evaluation matrix of each criterion and the factors it controls can be obtained. Therefore, the process of calculating the relative order weights of each factoru1,u2,⋯,un according to the reference matrix is called sorting according to a criterion. There are many calculation methods for the weights w1,w2,⋯,wn, among which the signature root method is a relatively mature and widely used method in the AHP, which is of great significance to the theoretical and practical development of the AHP.


### 3.3.2. Method for Finding Positive and Negative Matrix Sorting Vector

For positive matrices, there is a simple algorithm (power method) to find the eigenvectors. The following theorem provides the theoretical basis for the power method. Letn be a matrix where V is the eigenvector corresponding to the largest eigenvalue of A and c is a constant. If x=e (e is a unit vector), then W is the normalized eigenvector corresponding to the largest eigenvalue of A, hereinafter referred to as the weight vector or the sorting vector.In the first step, normalize the column items of the judgment matrix:(3)A∼ij=aij∑i=1naij.In the second step, theA∼ij by line:
(4)w∼=∑j=1na1j∑i=1naij,∑j=1na2j∑i=1naij,⋯∑j=1nanj∑i=1naij,T.In the third step, thew∼ after normalization:
(5)W=ω1,ω2,⋯ωnT.In the fourth step,λ is the largest eigenvalue of A:
(6)λ=1n∑i=1nAWiωi.In the first step, normalize the column vector of the judgment matrix.In the second step, theAij∼ by row:
(7)W¯=∏j=1na1j∑i=1naij1/n,∏j=1na2j∑i=1naij1/n,⋯,∏j=1nanj∑i=1naij1/nT.In the third step, thew∼ after normalization:
(8)W=ω1,ω2,⋯ωnT.In the fourth step,λ is the largest eigenvalue of A:
(9)λ=1n∑i=1nAWiωi.


### 3.3.3. Consistency Check

The complexity of objective things makes our judgments subjective and one-sided, so that every comparison and judgment cannot require exactly the same standard of thinking. Therefore, we do not require allnn+1/2 equations to be consistent when constructing the base matrix. However, it may also be that A and B are more important, Bis more important than C, and Cis more important than A, and this comparison is very inconsistent. When we compare rating matrices, we do not need to require ratings to be consistent. But a confusing and untenable comparative evaluation matrix can lead to wrong decisions, so we want the evaluations to be generally consistent. The above method for calculating weights is questionable when the estimated matrix deviates too much from the consistency. Therefore, when sorting each level by one criterion, it is necessary to check for consistency. Let A be a positive and inverse matrix of order n, we know from the theorem.
(10)AW=λmaxW,andλmax≥n.Likeλmax is much larger than n, then the degree of inconsistency of A is such that
(11)CI=λmax−nn−1.Inλmax, the largest eigenvalue is A, and CI can be used as a quantitative standard to measure the degree of inconsistency, which is called the consistency index. When CR<0.1, the consistency of the reference matrix is considered acceptable; otherwise, the evaluation matrix must be checked accordingly.TheCR value is less than 0.1, which meets the judgment requirements, indicating that the results have good consistency, and vice versa.


## 3.3.1. Sorting under a Single Criterion

The AHP database is a comparative assessment matrix. Because each criterion controls multiple factors at the next level, a comparative evaluation matrix of each criterion and the factors it controls can be obtained. Therefore, the process of calculating the relative order weights of each factoru1,u2,⋯,un according to the reference matrix is called sorting according to a criterion. There are many calculation methods for the weights w1,w2,⋯,wn, among which the signature root method is a relatively mature and widely used method in the AHP, which is of great significance to the theoretical and practical development of the AHP.


## 3.3.2. Method for Finding Positive and Negative Matrix Sorting Vector

For positive matrices, there is a simple algorithm (power method) to find the eigenvectors. The following theorem provides the theoretical basis for the power method. Letn be a matrix where V is the eigenvector corresponding to the largest eigenvalue of A and c is a constant. If x=e (e is a unit vector), then W is the normalized eigenvector corresponding to the largest eigenvalue of A, hereinafter referred to as the weight vector or the sorting vector.In the first step, normalize the column items of the judgment matrix:(3)A∼ij=aij∑i=1naij.In the second step, theA∼ij by line:
(4)w∼=∑j=1na1j∑i=1naij,∑j=1na2j∑i=1naij,⋯∑j=1nanj∑i=1naij,T.In the third step, thew∼ after normalization:
(5)W=ω1,ω2,⋯ωnT.In the fourth step,λ is the largest eigenvalue of A:
(6)λ=1n∑i=1nAWiωi.In the first step, normalize the column vector of the judgment matrix.In the second step, theAij∼ by row:
(7)W¯=∏j=1na1j∑i=1naij1/n,∏j=1na2j∑i=1naij1/n,⋯,∏j=1nanj∑i=1naij1/nT.In the third step, thew∼ after normalization:
(8)W=ω1,ω2,⋯ωnT.In the fourth step,λ is the largest eigenvalue of A:
(9)λ=1n∑i=1nAWiωi.


## 3.3.3. Consistency Check

The complexity of objective things makes our judgments subjective and one-sided, so that every comparison and judgment cannot require exactly the same standard of thinking. Therefore, we do not require allnn+1/2 equations to be consistent when constructing the base matrix. However, it may also be that A and B are more important, Bis more important than C, and Cis more important than A, and this comparison is very inconsistent. When we compare rating matrices, we do not need to require ratings to be consistent. But a confusing and untenable comparative evaluation matrix can lead to wrong decisions, so we want the evaluations to be generally consistent. The above method for calculating weights is questionable when the estimated matrix deviates too much from the consistency. Therefore, when sorting each level by one criterion, it is necessary to check for consistency. Let A be a positive and inverse matrix of order n, we know from the theorem.
(10)AW=λmaxW,andλmax≥n.Likeλmax is much larger than n, then the degree of inconsistency of A is such that
(11)CI=λmax−nn−1.Inλmax, the largest eigenvalue is A, and CI can be used as a quantitative standard to measure the degree of inconsistency, which is called the consistency index. When CR<0.1, the consistency of the reference matrix is considered acceptable; otherwise, the evaluation matrix must be checked accordingly.TheCR value is less than 0.1, which meets the judgment requirements, indicating that the results have good consistency, and vice versa.


## 3.4. Hierarchical Total Sorting

Computing the relative importance scale (also known as the ranking weight vector) of all elements of the same level to the highest level (overall target) is called the overall ranking of the level.


### 3.4.1. Steps of Hierarchical Total Sorting

(1)
Calculate the relative weight from the weight vector of all factors in the same layer to the highest layer; this process is carried out layer by layer from top to bottomSuppose that by calculating thek-th layer, there are k−1 layers with nk−1 a vector of sorting weights obtained by elements of elements relative to the total objects
(12)ωk−1=ω1k−1,ω2k−1,⋯,ωnk−1k−1TLayerKnk elements, they depend on some factors from the previous layer (k−1 layer) ui the full vector for single criterion sort is:
(13)pik=ω1ik,ω2ik,⋯,ωnkikT.For the correspondence with no dominance relation to thei-th element of the k−1 layer, uij value is 0.
(2)
Thek-th layer nk the sorting weight vector of elements relative to the total target is:(14)ω1k,ω2k⋯,ωnkkT=p1k,p2k,⋯,pk−1kωk−1


### 3.4.2. Total Ranking Consistency Check

When one compares the elements of each level, even if each level uses essentially the same benchmark, there may still be differences between the levels accumulated in the overall ranking of the levels calculated step-by-step, and it is necessary to test whether the accumulation of this difference scale has any effect on the overall model. Significantly, the testing process is called Hierarchical Universal Classification Conformance Testing.Assuming that thej-th factor of the (k−1-th layer is the reference standard, the first-level and first-level consistency indicators for the pairwise comparison of each factor in the k-th layer are:
(15)ωk−1representsthetotalrankingvectorofthetotaltargetofthek−1layerRIk=RIk−1·ωk−1,CRk=CRk−1+CIkRIk3≤k≤n.IfCRk<0.1, it can be considered that the evaluation model has achieved local satisfaction at the k-layer level.


## 3.4.1. Steps of Hierarchical Total Sorting

(1)
Calculate the relative weight from the weight vector of all factors in the same layer to the highest layer; this process is carried out layer by layer from top to bottomSuppose that by calculating thek-th layer, there are k−1 layers with nk−1 a vector of sorting weights obtained by elements of elements relative to the total objects
(12)ωk−1=ω1k−1,ω2k−1,⋯,ωnk−1k−1TLayerKnk elements, they depend on some factors from the previous layer (k−1 layer) ui the full vector for single criterion sort is:
(13)pik=ω1ik,ω2ik,⋯,ωnkikT.For the correspondence with no dominance relation to thei-th element of the k−1 layer, uij value is 0.
(2)
Thek-th layer nk the sorting weight vector of elements relative to the total target is:(14)ω1k,ω2k⋯,ωnkkT=p1k,p2k,⋯,pk−1kωk−1


## 3.4.2. Total Ranking Consistency Check

When one compares the elements of each level, even if each level uses essentially the same benchmark, there may still be differences between the levels accumulated in the overall ranking of the levels calculated step-by-step, and it is necessary to test whether the accumulation of this difference scale has any effect on the overall model. Significantly, the testing process is called Hierarchical Universal Classification Conformance Testing.Assuming that thej-th factor of the (k−1-th layer is the reference standard, the first-level and first-level consistency indicators for the pairwise comparison of each factor in the k-th layer are:
(15)ωk−1representsthetotalrankingvectorofthetotaltargetofthek−1layerRIk=RIk−1·ωk−1,CRk=CRk−1+CIkRIk3≤k≤n.IfCRk<0.1, it can be considered that the evaluation model has achieved local satisfaction at the k-layer level.


## 3.5. Adjustment of Judgment Matrix

When a comparative judgment matrix deviates too much from consistency, its reliability is questionable, and the judgment matrix must be adjusted at this time. In practical applications, the judgment matrix needs to be adjusted many times before it can pass the consistency test. At present, there are many ways to modify the judgment matrix, which can be roughly divided into three categories:(i)
Experience adjustment method: Let experts readjust some elements of the judgment matrix. This kind of method has a certain degree of subjective arbitrariness and lacks theoretical scientific basis(ii)
Construct a completely consistent judgment matrix with a certain method and extract the information of the original judgment matrix and the completely consistent matrix through the formaldehyde method, so as to achieve the purpose of adjustment. Such methods have certain blindness(iii)
Using the relationship between changes in matrix elements and consistency, identify key elements that affect consistency and make adjustments. Such methods change less elements of the original judgment matrix and retain more original informationThe following is the third type of evaluation matrix adjustment method, which is called the forward-looking algorithm of adjusting the AHP to evaluate the consistency of the matrix. The specific algorithm is as follows:Construct the matrix: Determine the elements of matrixA, aij used is replaced by aikakj, and aik use 1/aikakj, the matrix obtained after substitution, namely, Aijk=aijk.
(16)astk=askakts=i,t=j1/askakts=j,t=iastother.Becauseaskakt may be greater than 9, 1/askakt may be less than 1/9. This is inconsistent with the definition of judging oranges and may need to be fine-tuned, as follows:
(17)astk=Faskakts=i,t=j1/Faskakts=j,t=iastother.In,(18)Fx=9x≥9x1≤x<911/x1/9≤x<11/90<x<1/9.CalculationΔijk and Δij:
(19)Δijk=C.RA−C.RAijk,k≠i,j;k∈N.There aren−2 improvement degrees, such as Δijk=C.RA−C.RAijk≤0. It shows that the k-th adjustment does not help or even hinders the consistency of the judgment matrix, so it is set to 0 in the algorithm display.Δij is the element in the judgment matrix Aaij the n-2 maximum possible improvements. LikeΔij=0, all adjustment directions of aij do not help to improve the consistency of the judgment matrix.
(20)Δij=maxk∈Nk≠i,jC.RA−C.RAijk.CalculationTijTij is the adjustment strategy number corresponding to the maximum possible improvement and Dij degree of aij to moment consistency. If Dij =0, it means that all the adjustment directions of aij do not help to improve the consistency of the matrix.
(21)Tij=argmaxk∈Nk≠i,jC.RA−C.RAijk.(1) Adjust the empirical method. Let experts readjust some elements of the judgment matrix, but this method does have a theoretical basis. (2) Construct a consistent judgment matrix by the method, and extract the information of the original and consistent matrix by the formaldehyde method, and achieve the purpose of adjustment all the time. (3) Using the relationship between matrix element changes and consistency, determine and adjust the elements that affect consistency.


## 3.6. Calculation of Evaluation Set

Establishing an index set according to the evaluation index systemX=x1,x2⋯xmTand weight setw=w1,w2,⋯wm, the relationship between the evaluation index set Y, the factor set, and the weight set is:
(22)Y=Wn1TX=∑i=1mwixi,where Y is the final evaluation value of talent training.(1) Establish a hierarchical structure of the system. (2) Construct a pairwise comparison judgment matrix. (3) Calculate the sorting weight vector of the next level to a certain criterion of the previous level. (4) Total sorting is to calculate the sorting weight vector of each scheme to the total system target.


## 4. The Whole Chain Linkage of AHP Model and CIPP Model Talent Training Analysis Index Analysis

Aiming at the analysis of the indicators of talent training in the whole chain linkage, the questionnaires were distributed through the network platform. A total of 900 questionnaires were distributed in this study, and a total of 856 questionnaires were recovered, which were distributed to college students and social groups, respectively. The number of questionnaires was 467, and the effective recovery rate was 93.4%. 400 questionnaires were distributed to the public, and 389 questionnaires were effectively recovered, with an effective recovery rate of 97.25%.


### 4.1. Construct the Whole Chain Linkage Talent Training Index System

To build the whole chain linkage talent training index system, first divide the whole chain linkage talent training index into two elements. The first-level indicators include five aspects: personality literacy, innovation literacy, emotional intelligence literacy, leadership and management literacy, and scientific literacy; two first-level indicators are, respectively, refined for the first-level indicators, and there are a total of 18 small indicators. The 18 impact factors are assigned and calculated to form a quantitative evaluation index, as shown in Table1.Table 1
The whole chain linkage talent training index system.
Primary elementSecondary elementsPersonalityIndependenceCareerBe realistic and diligentInnovation literacyInnovative mindCreative thinkingInnovative spiritEmotional intelligence literacyEmotional perceptionEmotional expression abilityEmotion regulation abilityLeadership and management literacyCommunication and resilienceTeamworkOrganization and coordination capacityScientific ethicsBasic knowledgeProfessional knowledgeGeneral knowledge


### 4.2. Satisfaction with Primary Indicators

Through the survey on the satisfaction of college students and social groups with their own first-level indicators, the satisfaction score is 5 out of 5 points, and the obtained data is analyzed. It can be seen from Figure2 that in terms of personality literacy, college students’ satisfaction with themselves is higher than the social population, the average satisfaction of college students is 4.2, and the average satisfaction of the social population is 3.8; in terms of innovation literacy, the satisfaction of college students is higher than that of the social population; the average satisfaction of college students is 3.9, and the social population is 3.2; in terms of emotional intelligence literacy, social groups are significantly higher than college students, with an average satisfaction rate of 3.4 for college students and 4.5 for social groups; in terms of leadership and management literacy, social groups are higher than college students, with an average satisfaction rate of 4, the social group is 4.3, and the difference between the two is not significant; in terms of scientific literacy, college students are higher than the social group; the average satisfaction of college students is 3.7, and the social group is 3.7.Figure 2
Average satisfaction level 1 indicator.


### 4.3. Importance of Primary Indicators

Through the choice of the respondents, statistics of the data and drawing into a bar chart can more intuitively see which respondents generally think are more important. As can be seen from Figure3, most people think that emotional intelligence literacy is very important, ranking first in “very important”, accounting for 67.13%; ranking second is leadership and management literacy, accounting for 58.47%; the proportion of innovation literacy and scientific literacy is not significantly different, and the importance of college students and social groups is similar. Among the “more important,” most people think that personality literacy is more important, accounting for 31.72%; among the “more important” is “scientific literacy”; most people think that scientific literacy is not very important, accounting for 37.03; there is little difference between innovation literacy and leadership and management literacy; among the unimportant, the number of people who choose “scientific literacy” is the largest, accounting for 7.34%.Figure 3
Importance of primary indicators.According to Figure3, it can be seen that most people think that emotional intelligence literacy is very important, ranking first in “very important”; in “more important,” most people think that personality literacy is more important; in “less important,” most people rated scientific literacy as less important; among “not important,” the highest number chose “scientific literacy.”


### 4.4. Analysis of Indicators Based on AHP

#### 4.4.1. Index Consistency Test Results

It can be seen from Table2 that the CR value of No. 1 is 0.086 and less than 0.1, which meets the judgment requirements; the CR value of No. 2 is 0.082 and less than 0.1, which meets the judgment requirements; the CR value of No. The CR value of 0.017 is less than 0.1, which meets the judgment requirements, indicating that the results have good consistency and all meet the judgment requirements.Table 2
Consistency test of first-level indicators.
Serial numberPersonalityInnovation literacyEmotional intelligenceLeadership and managementScientific ethicsCR10.5240.1410.2180.0550.0625.3870.08620.2210.4270.0930.060.1995.360.08230.1290.1010.3440.1150.3115.2820.06340.1690.2940.1470.2220.1695.0770.017


#### 4.4.2. Weights of Primary Indicators

According to the method, the weight of the first-level indicators is calculated, and the importance is sorted. As can be seen from Table3, among the five indicators, personal quality has the largest weight, with a weight of 0.25; the second is innovation literacy, with a weight of 0.23; the third is emotional intelligence, with a weight of 0.2; the fourth is leadership and management ability literacy, with a weight of 0.18; the fourth is scientific literacy, with a weight of 0.15.Table 3
Ranking of the weights of the first-level indicators.
Indicator nameWeightsPersonality0.25Innovation literacy0.23Emotional intelligence literacy0.2Leadership and management literacy0.18Scientific ethics0.15


#### 4.4.3. Ranking of Weight Values of Secondary Indicators

As shown in Figure4, the second-level weights are arranged from large to small as being realistic and diligent > career > organizational coordination ability = innovative thinking = professional knowledge > innovative awareness = emotional regulation ability = team awareness > independence = innovative spirit = communication and resilience > basic knowledge of emotions > emotional perception skills = emotional expression skills = general knowledge.Figure 4
Weights of secondary indicators.


#### 4.4.4. Comment Set for Each Secondary Indicator

Among personality qualities, the highest value of “very good” is independence, with a value of 0.2; the highest value of “good” is truth-seeking and diligence, and the value of “good” is 0.4; independence, the evaluation value is 0.2, and the “poor” evaluation value is the highest for professionalism, truth-seeking, and diligence, and the evaluation value is 0.15; in the innovation literacy, the “very good” evaluation value is the highest for innovation consciousness, and the evaluation value is 0.33; the highest value of “good” is innovation consciousness, and the value of “good” is 0.3, the highest value of “good” is innovative thinking, the value of “poor” is 0.36, and the value of “poor” is the spirit of innovation, and the evaluation value is 3; in the EQ literacy, the “very good” evaluation value is the highest emotional regulation ability, and the evaluation value is 0.32; the “good” evaluation value is the largest emotional expression ability, and the evaluation value is 0.43; the “good” highest evaluation value is emotional perception ability, and the evaluation value is 0.3; and the “poor” evaluation value is emotional perception ability and emotional regulation ability, and the evaluation value is 0.15; in leadership and management literacy, the “very good” highest comment value is communication ability and adaptability, and the comment value is 0.37; and the “good” comment value is the team awareness and organization and coordination ability, and the evaluation value is 0.37; the “better” evaluation value is the communication ability and adaptability, and the evaluation value is 0.25; the “poor” evaluation value is the communication ability and adaptability, organization and coordination ability. The evaluation value is 0.18; in the scientific literacy, the “very good” evaluation value is the largest for professional knowledge, and the evaluation value is 0.4; the “good” evaluation value is the largest professional knowledge, and the evaluation value is 0.3; the “good” evaluation value is the largest. The highest value is general knowledge, with a review value of 0.38, and the highest value of “poor” is basic knowledge, with a review value of 0.2, as shown in Table4.Table 4
Secondary index comment set.
Primary elementSecondary elementsVery goodGoodBetterPoorPersonalityBe realistic and diligent0.40.30.20.1Professionalism0.320.350.180.15Organization and coordination capacity0.30.40.150.15Innovation literacyCreative thinking0.330.350.20.12Professional knowledge0.20.240.360.2Innovative mind0.150.30.250.3Emotional intelligence literacyEmotion regulation ability0.30.250.30.15Teamwork0.250.430.220.1Independence0.320.280.250.15Leadership and management literacyInnovative spirit0.370.20.250.18Communication and resilience0.250.370.230.15Basic knowledge0.250.370.20.18Scientific ethicsEmotional perception0.30.280.220.2Emotional expression ability0.40.30.150.15General knowledge0.30.20.380.12


### 4.5. Analysis of Indicators Based on CIPP

#### 4.5.1. Hierarchical Single Sorting and Total Sorting Table

As can be seen from Table5, among the first-level indicators, the most weighted is personality literacy, with a weight of 0.3021; the second is innovation literacy, with a weight of 0.2317; the third is leadership and management literacy, with a weight of 0.1814; the first is scientific literacy, with a weight of 0.1724; the fourth is emotional intelligence literacy, with a weight of 0.1124. In the second-level indicators, the ranking weight is hierarchical. In personality literacy, the single ranking weight of career aspiration is the largest, with a weight of 0.4504; in innovation literacy, the single ranking weight of innovative thinking is the largest, with a weight of 0.4271; in emotional intelligence literacy, the single-ranked weight of emotional control ability is the largest, with a weight of 0.4992; in scientific literacy, the single-ranked weight of team awareness is the largest, with a weight of 0.4732; in leadership and management literacy, the single-ranked weight of professional knowledge is the largest, with a weight of 0.4517. In the total ranking weight of the hierarchy, the greatest is career, followed by innovative thinking, professional knowledge, basic knowledge, and innovative spirit.Table 5
Hierarchical single order and total order table.
Primary elementWeightsSecondary elementsHierarchical single sorting weightsHierarchical total ranking weightPersonality0.3021Be realistic and diligent0.22420.065Professionalism0.45040.0998Organization and coordination capacity0.32540.0651Innovation literacy0.2317Creative thinking0.24310.085Professional knowledge0.42710.0937Innovative mind0.32980.072Emotional intelligence literacy0.1124Emotion regulation ability0.25460.0518Teamwork0.24620.0651Independence0.49920.0511Leadership and management literacy0.1814Innovative spirit0.38740.0473Communication and resilience0.47320.0456Basic knowledge0.13940.0341Scientific ethics0.1724Emotional perception0.33460.0768Emotional expression ability0.45170.0926General knowledge0.21370.055


#### 4.5.2. First-Level Indicator Evaluation Level

As can be seen from Figure5, the highest evaluation level is personal literacy, accounting for 29.41%; the highest evaluation level is innovation literacy, accounting for 35.29%; the highest evaluation level is innovation literacy and emotional intelligence literacy. The ratio is 26.47%; the highest evaluation level is scientific literacy, with a ratio of 32.36%.Figure 5
Levels of first-level indicator evaluation.


## 4.1. Construct the Whole Chain Linkage Talent Training Index System

To build the whole chain linkage talent training index system, first divide the whole chain linkage talent training index into two elements. The first-level indicators include five aspects: personality literacy, innovation literacy, emotional intelligence literacy, leadership and management literacy, and scientific literacy; two first-level indicators are, respectively, refined for the first-level indicators, and there are a total of 18 small indicators. The 18 impact factors are assigned and calculated to form a quantitative evaluation index, as shown in Table1.Table 1
The whole chain linkage talent training index system.
Primary elementSecondary elementsPersonalityIndependenceCareerBe realistic and diligentInnovation literacyInnovative mindCreative thinkingInnovative spiritEmotional intelligence literacyEmotional perceptionEmotional expression abilityEmotion regulation abilityLeadership and management literacyCommunication and resilienceTeamworkOrganization and coordination capacityScientific ethicsBasic knowledgeProfessional knowledgeGeneral knowledge


## 4.2. Satisfaction with Primary Indicators

Through the survey on the satisfaction of college students and social groups with their own first-level indicators, the satisfaction score is 5 out of 5 points, and the obtained data is analyzed. It can be seen from Figure2 that in terms of personality literacy, college students’ satisfaction with themselves is higher than the social population, the average satisfaction of college students is 4.2, and the average satisfaction of the social population is 3.8; in terms of innovation literacy, the satisfaction of college students is higher than that of the social population; the average satisfaction of college students is 3.9, and the social population is 3.2; in terms of emotional intelligence literacy, social groups are significantly higher than college students, with an average satisfaction rate of 3.4 for college students and 4.5 for social groups; in terms of leadership and management literacy, social groups are higher than college students, with an average satisfaction rate of 4, the social group is 4.3, and the difference between the two is not significant; in terms of scientific literacy, college students are higher than the social group; the average satisfaction of college students is 3.7, and the social group is 3.7.Figure 2
Average satisfaction level 1 indicator.


## 4.3. Importance of Primary Indicators

Through the choice of the respondents, statistics of the data and drawing into a bar chart can more intuitively see which respondents generally think are more important. As can be seen from Figure3, most people think that emotional intelligence literacy is very important, ranking first in “very important”, accounting for 67.13%; ranking second is leadership and management literacy, accounting for 58.47%; the proportion of innovation literacy and scientific literacy is not significantly different, and the importance of college students and social groups is similar. Among the “more important,” most people think that personality literacy is more important, accounting for 31.72%; among the “more important” is “scientific literacy”; most people think that scientific literacy is not very important, accounting for 37.03; there is little difference between innovation literacy and leadership and management literacy; among the unimportant, the number of people who choose “scientific literacy” is the largest, accounting for 7.34%.Figure 3
Importance of primary indicators.According to Figure3, it can be seen that most people think that emotional intelligence literacy is very important, ranking first in “very important”; in “more important,” most people think that personality literacy is more important; in “less important,” most people rated scientific literacy as less important; among “not important,” the highest number chose “scientific literacy.”


## 4.4. Analysis of Indicators Based on AHP

### 4.4.1. Index Consistency Test Results

It can be seen from Table2 that the CR value of No. 1 is 0.086 and less than 0.1, which meets the judgment requirements; the CR value of No. 2 is 0.082 and less than 0.1, which meets the judgment requirements; the CR value of No. The CR value of 0.017 is less than 0.1, which meets the judgment requirements, indicating that the results have good consistency and all meet the judgment requirements.Table 2
Consistency test of first-level indicators.
Serial numberPersonalityInnovation literacyEmotional intelligenceLeadership and managementScientific ethicsCR10.5240.1410.2180.0550.0625.3870.08620.2210.4270.0930.060.1995.360.08230.1290.1010.3440.1150.3115.2820.06340.1690.2940.1470.2220.1695.0770.017


### 4.4.2. Weights of Primary Indicators

According to the method, the weight of the first-level indicators is calculated, and the importance is sorted. As can be seen from Table3, among the five indicators, personal quality has the largest weight, with a weight of 0.25; the second is innovation literacy, with a weight of 0.23; the third is emotional intelligence, with a weight of 0.2; the fourth is leadership and management ability literacy, with a weight of 0.18; the fourth is scientific literacy, with a weight of 0.15.Table 3
Ranking of the weights of the first-level indicators.
Indicator nameWeightsPersonality0.25Innovation literacy0.23Emotional intelligence literacy0.2Leadership and management literacy0.18Scientific ethics0.15


### 4.4.3. Ranking of Weight Values of Secondary Indicators

As shown in Figure4, the second-level weights are arranged from large to small as being realistic and diligent > career > organizational coordination ability = innovative thinking = professional knowledge > innovative awareness = emotional regulation ability = team awareness > independence = innovative spirit = communication and resilience > basic knowledge of emotions > emotional perception skills = emotional expression skills = general knowledge.Figure 4
Weights of secondary indicators.


### 4.4.4. Comment Set for Each Secondary Indicator

Among personality qualities, the highest value of “very good” is independence, with a value of 0.2; the highest value of “good” is truth-seeking and diligence, and the value of “good” is 0.4; independence, the evaluation value is 0.2, and the “poor” evaluation value is the highest for professionalism, truth-seeking, and diligence, and the evaluation value is 0.15; in the innovation literacy, the “very good” evaluation value is the highest for innovation consciousness, and the evaluation value is 0.33; the highest value of “good” is innovation consciousness, and the value of “good” is 0.3, the highest value of “good” is innovative thinking, the value of “poor” is 0.36, and the value of “poor” is the spirit of innovation, and the evaluation value is 3; in the EQ literacy, the “very good” evaluation value is the highest emotional regulation ability, and the evaluation value is 0.32; the “good” evaluation value is the largest emotional expression ability, and the evaluation value is 0.43; the “good” highest evaluation value is emotional perception ability, and the evaluation value is 0.3; and the “poor” evaluation value is emotional perception ability and emotional regulation ability, and the evaluation value is 0.15; in leadership and management literacy, the “very good” highest comment value is communication ability and adaptability, and the comment value is 0.37; and the “good” comment value is the team awareness and organization and coordination ability, and the evaluation value is 0.37; the “better” evaluation value is the communication ability and adaptability, and the evaluation value is 0.25; the “poor” evaluation value is the communication ability and adaptability, organization and coordination ability. The evaluation value is 0.18; in the scientific literacy, the “very good” evaluation value is the largest for professional knowledge, and the evaluation value is 0.4; the “good” evaluation value is the largest professional knowledge, and the evaluation value is 0.3; the “good” evaluation value is the largest. The highest value is general knowledge, with a review value of 0.38, and the highest value of “poor” is basic knowledge, with a review value of 0.2, as shown in Table4.Table 4
Secondary index comment set.
Primary elementSecondary elementsVery goodGoodBetterPoorPersonalityBe realistic and diligent0.40.30.20.1Professionalism0.320.350.180.15Organization and coordination capacity0.30.40.150.15Innovation literacyCreative thinking0.330.350.20.12Professional knowledge0.20.240.360.2Innovative mind0.150.30.250.3Emotional intelligence literacyEmotion regulation ability0.30.250.30.15Teamwork0.250.430.220.1Independence0.320.280.250.15Leadership and management literacyInnovative spirit0.370.20.250.18Communication and resilience0.250.370.230.15Basic knowledge0.250.370.20.18Scientific ethicsEmotional perception0.30.280.220.2Emotional expression ability0.40.30.150.15General knowledge0.30.20.380.12


## 4.4.1. Index Consistency Test Results

It can be seen from Table2 that the CR value of No. 1 is 0.086 and less than 0.1, which meets the judgment requirements; the CR value of No. 2 is 0.082 and less than 0.1, which meets the judgment requirements; the CR value of No. The CR value of 0.017 is less than 0.1, which meets the judgment requirements, indicating that the results have good consistency and all meet the judgment requirements.Table 2
Consistency test of first-level indicators.
Serial numberPersonalityInnovation literacyEmotional intelligenceLeadership and managementScientific ethicsCR10.5240.1410.2180.0550.0625.3870.08620.2210.4270.0930.060.1995.360.08230.1290.1010.3440.1150.3115.2820.06340.1690.2940.1470.2220.1695.0770.017


## 4.4.2. Weights of Primary Indicators

According to the method, the weight of the first-level indicators is calculated, and the importance is sorted. As can be seen from Table3, among the five indicators, personal quality has the largest weight, with a weight of 0.25; the second is innovation literacy, with a weight of 0.23; the third is emotional intelligence, with a weight of 0.2; the fourth is leadership and management ability literacy, with a weight of 0.18; the fourth is scientific literacy, with a weight of 0.15.Table 3
Ranking of the weights of the first-level indicators.
Indicator nameWeightsPersonality0.25Innovation literacy0.23Emotional intelligence literacy0.2Leadership and management literacy0.18Scientific ethics0.15


## 4.4.3. Ranking of Weight Values of Secondary Indicators

As shown in Figure4, the second-level weights are arranged from large to small as being realistic and diligent > career > organizational coordination ability = innovative thinking = professional knowledge > innovative awareness = emotional regulation ability = team awareness > independence = innovative spirit = communication and resilience > basic knowledge of emotions > emotional perception skills = emotional expression skills = general knowledge.Figure 4
Weights of secondary indicators.


## 4.4.4. Comment Set for Each Secondary Indicator

Among personality qualities, the highest value of “very good” is independence, with a value of 0.2; the highest value of “good” is truth-seeking and diligence, and the value of “good” is 0.4; independence, the evaluation value is 0.2, and the “poor” evaluation value is the highest for professionalism, truth-seeking, and diligence, and the evaluation value is 0.15; in the innovation literacy, the “very good” evaluation value is the highest for innovation consciousness, and the evaluation value is 0.33; the highest value of “good” is innovation consciousness, and the value of “good” is 0.3, the highest value of “good” is innovative thinking, the value of “poor” is 0.36, and the value of “poor” is the spirit of innovation, and the evaluation value is 3; in the EQ literacy, the “very good” evaluation value is the highest emotional regulation ability, and the evaluation value is 0.32; the “good” evaluation value is the largest emotional expression ability, and the evaluation value is 0.43; the “good” highest evaluation value is emotional perception ability, and the evaluation value is 0.3; and the “poor” evaluation value is emotional perception ability and emotional regulation ability, and the evaluation value is 0.15; in leadership and management literacy, the “very good” highest comment value is communication ability and adaptability, and the comment value is 0.37; and the “good” comment value is the team awareness and organization and coordination ability, and the evaluation value is 0.37; the “better” evaluation value is the communication ability and adaptability, and the evaluation value is 0.25; the “poor” evaluation value is the communication ability and adaptability, organization and coordination ability. The evaluation value is 0.18; in the scientific literacy, the “very good” evaluation value is the largest for professional knowledge, and the evaluation value is 0.4; the “good” evaluation value is the largest professional knowledge, and the evaluation value is 0.3; the “good” evaluation value is the largest. The highest value is general knowledge, with a review value of 0.38, and the highest value of “poor” is basic knowledge, with a review value of 0.2, as shown in Table4.Table 4
Secondary index comment set.
Primary elementSecondary elementsVery goodGoodBetterPoorPersonalityBe realistic and diligent0.40.30.20.1Professionalism0.320.350.180.15Organization and coordination capacity0.30.40.150.15Innovation literacyCreative thinking0.330.350.20.12Professional knowledge0.20.240.360.2Innovative mind0.150.30.250.3Emotional intelligence literacyEmotion regulation ability0.30.250.30.15Teamwork0.250.430.220.1Independence0.320.280.250.15Leadership and management literacyInnovative spirit0.370.20.250.18Communication and resilience0.250.370.230.15Basic knowledge0.250.370.20.18Scientific ethicsEmotional perception0.30.280.220.2Emotional expression ability0.40.30.150.15General knowledge0.30.20.380.12


## 4.5. Analysis of Indicators Based on CIPP

### 4.5.1. Hierarchical Single Sorting and Total Sorting Table

As can be seen from Table5, among the first-level indicators, the most weighted is personality literacy, with a weight of 0.3021; the second is innovation literacy, with a weight of 0.2317; the third is leadership and management literacy, with a weight of 0.1814; the first is scientific literacy, with a weight of 0.1724; the fourth is emotional intelligence literacy, with a weight of 0.1124. In the second-level indicators, the ranking weight is hierarchical. In personality literacy, the single ranking weight of career aspiration is the largest, with a weight of 0.4504; in innovation literacy, the single ranking weight of innovative thinking is the largest, with a weight of 0.4271; in emotional intelligence literacy, the single-ranked weight of emotional control ability is the largest, with a weight of 0.4992; in scientific literacy, the single-ranked weight of team awareness is the largest, with a weight of 0.4732; in leadership and management literacy, the single-ranked weight of professional knowledge is the largest, with a weight of 0.4517. In the total ranking weight of the hierarchy, the greatest is career, followed by innovative thinking, professional knowledge, basic knowledge, and innovative spirit.Table 5
Hierarchical single order and total order table.
Primary elementWeightsSecondary elementsHierarchical single sorting weightsHierarchical total ranking weightPersonality0.3021Be realistic and diligent0.22420.065Professionalism0.45040.0998Organization and coordination capacity0.32540.0651Innovation literacy0.2317Creative thinking0.24310.085Professional knowledge0.42710.0937Innovative mind0.32980.072Emotional intelligence literacy0.1124Emotion regulation ability0.25460.0518Teamwork0.24620.0651Independence0.49920.0511Leadership and management literacy0.1814Innovative spirit0.38740.0473Communication and resilience0.47320.0456Basic knowledge0.13940.0341Scientific ethics0.1724Emotional perception0.33460.0768Emotional expression ability0.45170.0926General knowledge0.21370.055


### 4.5.2. First-Level Indicator Evaluation Level

As can be seen from Figure5, the highest evaluation level is personal literacy, accounting for 29.41%; the highest evaluation level is innovation literacy, accounting for 35.29%; the highest evaluation level is innovation literacy and emotional intelligence literacy. The ratio is 26.47%; the highest evaluation level is scientific literacy, with a ratio of 32.36%.Figure 5
Levels of first-level indicator evaluation.


## 4.5.1. Hierarchical Single Sorting and Total Sorting Table

As can be seen from Table5, among the first-level indicators, the most weighted is personality literacy, with a weight of 0.3021; the second is innovation literacy, with a weight of 0.2317; the third is leadership and management literacy, with a weight of 0.1814; the first is scientific literacy, with a weight of 0.1724; the fourth is emotional intelligence literacy, with a weight of 0.1124. In the second-level indicators, the ranking weight is hierarchical. In personality literacy, the single ranking weight of career aspiration is the largest, with a weight of 0.4504; in innovation literacy, the single ranking weight of innovative thinking is the largest, with a weight of 0.4271; in emotional intelligence literacy, the single-ranked weight of emotional control ability is the largest, with a weight of 0.4992; in scientific literacy, the single-ranked weight of team awareness is the largest, with a weight of 0.4732; in leadership and management literacy, the single-ranked weight of professional knowledge is the largest, with a weight of 0.4517. In the total ranking weight of the hierarchy, the greatest is career, followed by innovative thinking, professional knowledge, basic knowledge, and innovative spirit.Table 5
Hierarchical single order and total order table.
Primary elementWeightsSecondary elementsHierarchical single sorting weightsHierarchical total ranking weightPersonality0.3021Be realistic and diligent0.22420.065Professionalism0.45040.0998Organization and coordination capacity0.32540.0651Innovation literacy0.2317Creative thinking0.24310.085Professional knowledge0.42710.0937Innovative mind0.32980.072Emotional intelligence literacy0.1124Emotion regulation ability0.25460.0518Teamwork0.24620.0651Independence0.49920.0511Leadership and management literacy0.1814Innovative spirit0.38740.0473Communication and resilience0.47320.0456Basic knowledge0.13940.0341Scientific ethics0.1724Emotional perception0.33460.0768Emotional expression ability0.45170.0926General knowledge0.21370.055


## 4.5.2. First-Level Indicator Evaluation Level

As can be seen from Figure5, the highest evaluation level is personal literacy, accounting for 29.41%; the highest evaluation level is innovation literacy, accounting for 35.29%; the highest evaluation level is innovation literacy and emotional intelligence literacy. The ratio is 26.47%; the highest evaluation level is scientific literacy, with a ratio of 32.36%.Figure 5
Levels of first-level indicator evaluation.


## 5. Conclusion

Under the current development trend, it is very necessary to carry out the evaluation of the entire chain linkage talent training system with the fuzzy AHP model, which has a great effect on the quality of talents. By using the fuzzy AHP model and the CPII model, and comparing the results of the two models, the weights of each indicator in talent training are obtained. The analysis shows that in the construction of the entire chain linkage talent training, it is necessary to strengthen personal quality, innovation quality, emotional intelligence quality, and emotional intelligence. The cultivation of leadership and management ability literacy, scientific literacy, etc. can better reflect the characteristics of talents; however, the AHP model can more comprehensively analyze the evaluation of the entire chain linkage talent training system. It is very simple and convenient to use AHP as an evaluation for analyzing the entire chain linkage talent training system, and the results are also credible, which can provide a reference for the society to build talent training indicators, and can also serve as a basis for decision-making by the education department. Realize the construction of a “whole chain” linkage talent training system, so as to build an institutional environment and a good atmosphere that are conducive to the growth and functioning of skilled talents.



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*Source: 7106274-2022-07-06.xml*