# Innovative College Students’ Educational Management Mode Based on BP Neural Network

Authors: Yuanyuan Zhang
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

With the advancement of society, the necessities for school graduates are getting increasingly elevated. In this unique situation, the conventional showing the board model in schools and colleges has not been able to adjust to the pattern of present day advancement and should be improved and developed likewise. By advancing the instructive administration model, this paper expects to work on the idea, conduct, and advancement capacity of undergrads to address the issues of social turn of events. In light of this, this paper principally concentrates on the issues existing in the instruction of the board of undergrads and the comparing imaginative models. This paper takes Wenzhou University students as the research object, combining qualitative analysis with quantitative analysis. This paper assembles a general travel industry proficient development and business venture schooling assessment framework based on BP brain organization. It lessens the impedance brought about by human elements in the assessment cycle and guarantees the precision of the assessment results. This paper improves the research and analysis framework of related fields, enriches the research results of innovation and entrepreneurship education evaluation in tourism, and has important theoretical value for improving the evaluation model in this field. The exploratory aftereffects of this paper show that the thorough organization yield esteem is 0.7261 as per the information gathered by Wenzhou University utilizing the poll review. It demonstrates that the advancement and business training of Wenzhou University is at a medium level, and there is still a lot of opportunity to get better.


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## Body

## 1. Introduction

With the advancement of the times, the gathering of undergrads is continually evolving. As of now, the gathering of Chinese understudies is mostly the undergrads after the “10th Five-Year Plan.” Due to the changes in birth conditions and growth environment, their psychological characteristics and learning needs have undergone great changes, giving college education management for college students. As a pertinent supervisor, its requirements improve and develop the significant schooling of the executive model to be perceived and cherished by the general public as indicated by the attributes of the times and the particular necessities of current understudies. BP brain network has great nonlinear planning capacity and can deal with nonlinear assessment file well. Over 80% of the applications utilize the BP brain organization or further develop the BP brain organization to accomplish the reason for assessment. The more regularly utilized capacity assessment technique is the combination of the improvement calculation and the BP brain organization.Educational management is a complex system engineering, and management innovation is the process of self-transformation of educational subjects. Finally, on the basis of the innovative education management model, this paper improves the effects of the traditional education management model in all aspects of the education process. It mainly adopts the concept of innovation and advanced technology to continuously stimulate people’s creativity and promote the continuous innovation of education. In light of the most recent public advancement procedure, this paper centers around the assessment of development and business venture training for the travel industry study schools and colleges. This paper is devoted to giving a reference to the assessment techniques for advancement and business schooling in universities and colleges in China. It further works fair and square of expert training according to the viewpoints of advancement awareness and pioneering capacity.The innovation of this paper is that the BP neural network is introduced into the innovative education management mode of college students, which has certain theoretical significance for other comprehensive applications of BP neural network research.


## 2. Related Work

In fact, the construction of neural network model has become a research hotspot. Many scholars have used neural network models in various researches. Ma et al. gathered 14 arrangements of exploratory information on heat move coefficients in the supercritical water pressure range. Based on trial information, they laid out a BP brain network expectation model to decide the hotness move coefficient of supercritical water. They utilized the BP brain network forecast model to concentrate because of boundaries, for example, heat stream, mass stream, pipe breadth, and tension on the hotness move coefficient of supercritical water [1]. Wang et al. established a wind power range prediction model based on the multioutput characteristics of BP neural network. They proposed an optimization criterion that takes into account the prediction interval information. They then, at that point, utilized a superior molecule swarm streamlining calculation to upgrade the model. The recreation consequences of reasonable models show that the proposed breeze power range expectation model can successfully foresee the result power span and give choice help to matrix dispatchers [2]. Really distinguishing remote diverts in various situations or regions can take care of the issue of multipath obstruction during remote correspondence. Li et al. concentrate various attributes of the remote channel, for example, multipath number, endlessly postpone spread, as indicated by the appearance time and the got signal strength, and lay out the component vector set of the remote channel. It is utilized to prepare a back-spread (BP) brain organization to recognize different remote channels [3]. Li et al. combine chaotic algorithms with genetic algorithms. They proposed a new gesture recognition method to solve the shortcomings of BP neural network in gesture recognition, which is easy to fall into local minimum and slow convergence [4]. Using elastic wave theory, Lü et al. analyzed the effects of parameters such as density, stress, and strain on the velocities of longitudinal and shear waves. They then tested P- and S-wave velocities under different lithology, saturation, ambient, and axial pressure conditions [5]. Wang et al. embraced the Mixed Mind Evolutionary Algorithm BP Neural Network Strategy (MEA-BP). They concentrate on how BP brain organization (BPnn) advancement with MEA further develops the speculation capacity and consistency of BPnn. The MEA-BP model joins the nearby inquiry capacity of BP brain organization and the worldwide hunt capacity of MEA, which evades the issues of untimely union and unfortunate forecast impact [6]. To work on the viability of BP brain network in 2D point of interaction thickness reversal, Zhang et al. utilized hereditary calculation to streamline the determination cycle of BP brain network loads and edges, in order to get better reversal results [7]. The downside of these studies, however, is that the considerations are not comprehensive enough to adapt to more complex situations, and precision needs to be improved.


## 3. Methods Based on Neural Network

### 3.1. Artificial Neural Network

#### 3.1.1. Artificial Neural Network Model

Artificial neural network (ANN), referred to as neural network, is an information processing system inspired by the structure and function of biological neurons. A neural network is a complex network composed of many connected neurons. After adjusting the weights between neurons and modeling and simulating the input sample data, it finally has the ability to solve and deal with problems [8]. Figure 1 is a flow chart of neural network learning.Figure 1
Neural network learning flow chart.The basic unit of artificial neural network is called artificial neuron, and its model is shown in Figure2. It is similar to a nonlinear threshold device with multiple inputs and only one output. Define the input vector for the neuron:
(1)A=A1,A2,A3,⋯,AnT.Figure 2
Neuron network model.Define the weight vectorε:
(2)ε=ε1,ε2,ε3,⋯,εnT.∂ is the edge of the neuron, and f is the enactment capacity of the neuron. Then, at that point, the neuron yield vector B is
(3)B=∑jNAjεj+∂.


#### 3.1.2. Error Correction Learning (Delta Rule)

AllowBin to be the genuine organization result of neuron I at time n when An is information, and Rin addresses the result of the relating test; then, at that point, the blunder can be composed as
(4)Ein=Rin−Bin.Blunder revision learning is to make the goal work in light ofEin arrive at the base worth, with the goal that the real result of each result neuron in the brain network is genuinely nearest to the result of the example [9]. The most generally utilized objective capacity is the mean square blunder measure, characterized as
(5)Q=E12∑iEi2n,where E is the expectation operator. Because when using Q directly as the objective function, it is necessary to count the characteristics of the whole process. In order to solve this difficulty, we generally replace Q with the instantaneous value ωn of Q at time n, namely,
(6)ωn=12∑iEi2n.Using the steepest gradient descent method, we get(7)ΔTijn=ηnEinAjn,where ηn>0 is the learning step size.The brain network models for which this learning rule is effectively applied are exceptionally broad, for example, the least complex perceptron learning calculation, which is likewise the most crude utilization of brain organizations. The most exemplary application is the back-engendering learning calculation, otherwise called the BP calculation [10].


#### 3.1.3. Hebb Learning

When the activation of neurons at both ends of a synapse is synchronized, the strength of the connection should increase, and vice versa, mathematically expressed as(8)ΔTijn=FBin,Ajn,where Bin,Ajn are the states of the neurons at both ends of Tij, respectively, and the most commonly used cases are
(9)ΔTijn=ηBinAjn.Due to the correlation betweenΔT and Bin,Ajn, it is also called the correlation learning rule.There are two kinds of networks that apply this rule: discrete Hopfield network and continuous Hopfield network.


#### 3.1.4. Competitive Learning

In the serious learning of the organization, each result unit contends with one another; lastly, only one of the most grounded can be initiated [11]. The principles are addressed by the accompanying recipe:
(10)ΔTn=ηAj−Tjk,IfneuronJcompetitionwins,0,IfneuronJcompetitionfails.At the point when the climate wherein the learning framework is found is steady, hypothetically, the factual attributes of the climate can be learned through administered learning, and these measurable qualities can be recalled by the brain network as experience. Assuming that the climate is nonfixed, the typical managed learning does not can track such changes, and to take care of this issue requires the organization to have a specific versatile capacity [12].


#### 3.1.5. Artificial Neural Network Classification

The artificial neural network has strong plasticity, and the arbitrary combination of network structure and activation function can construct different forms of artificial neural network.The artificial neural network classification diagram is shown in Figure3.Figure 3
Artificial neural network classification diagram.
(a)
Forward network structure diagram(b)
Feedback network structure diagram(1) Forward Network. The neurons in the forward network are organized progressively, and every neuron is simply associated with the past layer of neurons. The forward network structure is shown in Figure 3(a) [13]. The training data flows from the input layer through the hidden layer to the output layer and propagates in hierarchical order. Each layer of neurons will only receive input signals from the neurons in the previous layer, and subsequent layers will not bring feedback to the previous layers. The well-known perceptron network and BP network are both types of forward networks.(2) Feedback Network. A feedback network refers to a neural network that contains one or more feedback loops. The feedback network structure is shown in Figure 3(b). In the feedback network, there is a feedback signal between the output layer and the input layer. The output value of the network will become the input value of the network again, so that the data information circulates in the entire network until the training results meet the requirements of the network.(3) There Is a Tutor to Learn the Network. Learning with a tutor means that in network learning, a given target output is required for network training. The neural network does not know the external environment, and the artificially given target output samples are equivalent to “mentors,” which represent the best corresponding effect of neural network training. It adjusts the network parameters according to the target output, so that the network output approaches the termination condition. The BP network used in this paper is a typical tutored learning network [14].(4) Tutorless Learning Network. In contrast to tutored learning networks, tutored learning does not have external tutors or evaluations to view the learning process. Its learning methods are divided into reinforcement learning and self-organizing learning. In reinforcement learning, there is a mapping relationship between the output and the output of the network, and the learning and training of the network are realized according to the performance scalar index that minimizes its continuous interaction with the external environment. In self-organizing learning, a measure of the quality of the network learning representation method is provided, and the network parameters are adjusted according to the provided standard to achieve optimal results.


### 3.2. BP Neural Network and Algorithm

BP (back propagation) brain network is a multifacet forward network with coach learning in light of blunder back engendering calculation preparing. It is made out of numerous neurons associated with one another as indicated by specific standards [15]. The BP brain network model is displayed in Figure 4. BPNN is basically the issue of tackling the base worth of the blunder capacity, and it is a significant and exemplary calculation.Figure 4
Structure of BP network.


#### 3.2.1. Basic Structure of BP Neural Network

The basic BP network algorithm contains two propagation directions: the forward propagation of the input signal and the back propagation of the output error. That is to say, when calculating the actual network output, it is carried out in the direction from input to output, but the correction of the weights and thresholds of each layer of the network is carried out in the reverse direction from output to input.The sign of BP brain network is forward conduction. The sign is sent from the info hub to the secret layer hub through weighting and capacity change, and its worth is(11)Dj=Fnetj,j=1,2,⋯,m,(12)netj=∑i=0nUijGi,j=1,2,⋯,m.For the BP network with multihidden layer structure, the signal is transmitted from the hidden layer of the previous layer to the next hidden layer; for the single hidden layer structure shown in the figure, the signal is directly transmitted from the hidden layer to the output layer, as follows:(13)Vk=Fnetk,j=1,2,⋯,k,(14)netk=∑j=0mRjkGj,k=1,2,⋯,s.Among them,FX is called the transfer function, and the unipolar sigmoid function (hyperbolic tangent function) is generally used:
(15)FX=11+E−X.Make it differentiable and continuous, and(16)F′X=FX1−FX.Sometimes, depending on the application needs, a bipolar sigmoid function can also be used:(17)FX=1−E−X1+E−X.


#### 3.2.2. BP Learning Algorithm

The core idea of the BP learning algorithm is to repeat the forward conduction and reverse conduction process of the error until the output error meets the design requirements [16].The expected output of the neural network isY=Y1,Y2,⋯,YlT, and the output error e is defined as
(18)e=12Y−L2=12∑k=1sYk−Lk2.Expand the formula layer by layer in reverse. For the hidden layer, there are(19)e=12∑k=1sYk−Fnetk2=12∑k=1sYk−F∑j=0mVjkDj2.Expanding further to the output layer, there are(20)e=12∑k=1sYk−F∑j=0mVjkFnetk2=12∑k=1sYk−F∑j=0mVjkF∑i=0nUijXi2.It can be seen from the formula that the output errore is affected by the connection weights Vjk and Uij between the layers. The purpose of BP learning is to adjust the weight matrices V and U to reduce the output error e to the minimum value. Let the adjustment of the weights be proportional to the gradient descent of the error, namely,
(21)ΔVjk=−η∂e∂vjk,(22)ΔUij=−η∂e∂Uij.In the two formulas, the constantη is the learning rate, which can affect the speed of weight adjustment, which in turn affects the speed of the entire learning process. When η∈0,1 and η are large, it is easier to jump out of the local minimum interval of the output error e, and the learning speed is fast, but the adjustment accuracy is poor [17].Substitute the expression ofE in formulas (19) and (20) into the gradient adjustment formulas (21) and (22), and the weight adjustment formula of the 3-layer BPNN can be obtained:
(23)ΔVjk=ηYk−LkLk1−LkDj,(24)ΔUij=η∑k=1sYk−LkLk1−LkVjkDj1−DjXi.The derivative of the unipolar sigmoid function, formula (16), is applied. For multihidden layer BPNN, it is only necessary to reversely derive the weight adjustment formula according to the rules [18]. The flow chart of the standard BP network program is shown in Figure 5.Figure 5
BP algorithm program flow chart.BP neural network has three typical characteristics: (1) It is made out of numerous layers, and the neurons in nearby layers are completely associated, and the neurons in a similar layer are not associated. (2) The organization preparing process is done by the mistake back engendering calculation. The sign streams from the information layer of the organization through the secret layer to the result layer. When the network weights are trained, the network weights are corrected reversely from the output layer to the input layer in the direction of reducing the error; furthermore, this interaction is rehashed until the mistake will in general be tiny. (3) The enactment capacity of the organization is differentiable; typically, a sigmoid capacity or a direct capacity is utilized. Among them, the sigmoid capacity is isolated into two sorts: log-sigmoid capacity and tan-sigmoid capacity [19].The bends of the log-sigmoid capacity and the tan-sigmoid capacity are displayed in Figures6(a) and 6(b), individually. As can be seen from the figure, the sigmoid function is smooth and differentiable, and it has better fault tolerance than the linear function in classification. The sigmoid function maps the input data from the range of −∞,+∞ to the (-1,1) or (0,1) interval and has the characteristics of nonlinear amplification.Figure 6
Activation function image.
(a)
Log-sigmoid function(b)
Tan-sigmoid function


### 3.3. Application of Artificial Neural Network in Innovation and Entrepreneurship Education Evaluation

#### 3.3.1. Status

This paper sorts out the literature on teaching quality evaluation of BP neural network in innovation and entrepreneurship education by many scholars and reflects the current development trend. It seeks new directions, grasps new trends, and brings new enlightenment to the research of this paper. To begin with, the BP brain network model utilized in the showing quality assessment of advancement and business instruction has been somewhat full grown, which demonstrates the appropriateness of the strategy and elucidates the comparing finishing joins. Secondly, in order to better promote innovation and entrepreneurship education and carry out in-depth education reform, this paper has laid a good foundation and provided more diverse evaluation and selection methods [20].


#### 3.3.2. Defect Analysis and Improvement Demands of the Current Quality Evaluation of Innovation and Entrepreneurship Education

The formulation of innovation and entrepreneurship education should be carried out in accordance with the principles of scientificity, integration, systematicness, and objectivity and fully reflect the natural characteristics and attributes of the object being evaluated. However, there is a problem of blind quantification in the setting of China’s innovation and entrepreneurship evaluation system, and the observation points are not comprehensive, which cannot timely and accurately reflect the evaluation subject, and the evaluation indicators are unclear. The issues have genuinely impacted the assessment nature of advancement and business venture training partially and ruined the improvement of development and business schooling [21].In order to establish a perfect teaching quality evaluation system, we must first solve a problem of unity of opposites: who should evaluate and what is the evaluation content, that is, the choice of evaluation subject and evaluation object. In the traditional teaching quality evaluation system with a single evaluation subject, the evaluation content is not comprehensive, which increases the possibility of distortion of the final evaluation results.As should be visible from Table1, the assessment strategies have clear impediments and can assess one angle. High intricacy and thoroughness are the greatest attributes of development and business instruction, so the relating assessment exercises ought to likewise take on adaptable and different means [22].Table 1
Common evaluation patterns.
Evaluation methodAdvantageShortcomingApplicationStudent evaluationStudents evaluate teachers deeply and comprehensively according to their own learning feelingsStudents’ attitude towards evaluation will affect the evaluation results; students’ own learning level will affect their understanding of teaching objectivesIt is widely used and easy to operateTeacher self-evaluationLet teachers understand the gap with standards and promote teachers’ developmentIt is difficult to determine whether the evaluator is objective in the process of self-evaluationEncouraging teachers’ self-development is an opportunity for teachers to understand themselvesExpert evaluationIn terms of knowledge level and experience, they should far surpass students; the evaluation is serious and responsible with high credibilityIn the process of evaluation, we pay attention to generality and universality, and special links in teaching will be ignored to a certain extent; unable to fully understand teaching activities due to the limited number of lecturesWidely popularized in ChinaManager evaluationBe familiar with the evaluation criteria and be responsible for the evaluation processUnable to fully understand teaching activities due to the limited number of lecturesIt is used for award selection and random inspection of teaching quality


## 3.1. Artificial Neural Network

### 3.1.1. Artificial Neural Network Model

Artificial neural network (ANN), referred to as neural network, is an information processing system inspired by the structure and function of biological neurons. A neural network is a complex network composed of many connected neurons. After adjusting the weights between neurons and modeling and simulating the input sample data, it finally has the ability to solve and deal with problems [8]. Figure 1 is a flow chart of neural network learning.Figure 1
Neural network learning flow chart.The basic unit of artificial neural network is called artificial neuron, and its model is shown in Figure2. It is similar to a nonlinear threshold device with multiple inputs and only one output. Define the input vector for the neuron:
(1)A=A1,A2,A3,⋯,AnT.Figure 2
Neuron network model.Define the weight vectorε:
(2)ε=ε1,ε2,ε3,⋯,εnT.∂ is the edge of the neuron, and f is the enactment capacity of the neuron. Then, at that point, the neuron yield vector B is
(3)B=∑jNAjεj+∂.


### 3.1.2. Error Correction Learning (Delta Rule)

AllowBin to be the genuine organization result of neuron I at time n when An is information, and Rin addresses the result of the relating test; then, at that point, the blunder can be composed as
(4)Ein=Rin−Bin.Blunder revision learning is to make the goal work in light ofEin arrive at the base worth, with the goal that the real result of each result neuron in the brain network is genuinely nearest to the result of the example [9]. The most generally utilized objective capacity is the mean square blunder measure, characterized as
(5)Q=E12∑iEi2n,where E is the expectation operator. Because when using Q directly as the objective function, it is necessary to count the characteristics of the whole process. In order to solve this difficulty, we generally replace Q with the instantaneous value ωn of Q at time n, namely,
(6)ωn=12∑iEi2n.Using the steepest gradient descent method, we get(7)ΔTijn=ηnEinAjn,where ηn>0 is the learning step size.The brain network models for which this learning rule is effectively applied are exceptionally broad, for example, the least complex perceptron learning calculation, which is likewise the most crude utilization of brain organizations. The most exemplary application is the back-engendering learning calculation, otherwise called the BP calculation [10].


### 3.1.3. Hebb Learning

When the activation of neurons at both ends of a synapse is synchronized, the strength of the connection should increase, and vice versa, mathematically expressed as(8)ΔTijn=FBin,Ajn,where Bin,Ajn are the states of the neurons at both ends of Tij, respectively, and the most commonly used cases are
(9)ΔTijn=ηBinAjn.Due to the correlation betweenΔT and Bin,Ajn, it is also called the correlation learning rule.There are two kinds of networks that apply this rule: discrete Hopfield network and continuous Hopfield network.


### 3.1.4. Competitive Learning

In the serious learning of the organization, each result unit contends with one another; lastly, only one of the most grounded can be initiated [11]. The principles are addressed by the accompanying recipe:
(10)ΔTn=ηAj−Tjk,IfneuronJcompetitionwins,0,IfneuronJcompetitionfails.At the point when the climate wherein the learning framework is found is steady, hypothetically, the factual attributes of the climate can be learned through administered learning, and these measurable qualities can be recalled by the brain network as experience. Assuming that the climate is nonfixed, the typical managed learning does not can track such changes, and to take care of this issue requires the organization to have a specific versatile capacity [12].


### 3.1.5. Artificial Neural Network Classification

The artificial neural network has strong plasticity, and the arbitrary combination of network structure and activation function can construct different forms of artificial neural network.The artificial neural network classification diagram is shown in Figure3.Figure 3
Artificial neural network classification diagram.
(a)
Forward network structure diagram(b)
Feedback network structure diagram(1) Forward Network. The neurons in the forward network are organized progressively, and every neuron is simply associated with the past layer of neurons. The forward network structure is shown in Figure 3(a) [13]. The training data flows from the input layer through the hidden layer to the output layer and propagates in hierarchical order. Each layer of neurons will only receive input signals from the neurons in the previous layer, and subsequent layers will not bring feedback to the previous layers. The well-known perceptron network and BP network are both types of forward networks.(2) Feedback Network. A feedback network refers to a neural network that contains one or more feedback loops. The feedback network structure is shown in Figure 3(b). In the feedback network, there is a feedback signal between the output layer and the input layer. The output value of the network will become the input value of the network again, so that the data information circulates in the entire network until the training results meet the requirements of the network.(3) There Is a Tutor to Learn the Network. Learning with a tutor means that in network learning, a given target output is required for network training. The neural network does not know the external environment, and the artificially given target output samples are equivalent to “mentors,” which represent the best corresponding effect of neural network training. It adjusts the network parameters according to the target output, so that the network output approaches the termination condition. The BP network used in this paper is a typical tutored learning network [14].(4) Tutorless Learning Network. In contrast to tutored learning networks, tutored learning does not have external tutors or evaluations to view the learning process. Its learning methods are divided into reinforcement learning and self-organizing learning. In reinforcement learning, there is a mapping relationship between the output and the output of the network, and the learning and training of the network are realized according to the performance scalar index that minimizes its continuous interaction with the external environment. In self-organizing learning, a measure of the quality of the network learning representation method is provided, and the network parameters are adjusted according to the provided standard to achieve optimal results.


## 3.1.1. Artificial Neural Network Model

Artificial neural network (ANN), referred to as neural network, is an information processing system inspired by the structure and function of biological neurons. A neural network is a complex network composed of many connected neurons. After adjusting the weights between neurons and modeling and simulating the input sample data, it finally has the ability to solve and deal with problems [8]. Figure 1 is a flow chart of neural network learning.Figure 1
Neural network learning flow chart.The basic unit of artificial neural network is called artificial neuron, and its model is shown in Figure2. It is similar to a nonlinear threshold device with multiple inputs and only one output. Define the input vector for the neuron:
(1)A=A1,A2,A3,⋯,AnT.Figure 2
Neuron network model.Define the weight vectorε:
(2)ε=ε1,ε2,ε3,⋯,εnT.∂ is the edge of the neuron, and f is the enactment capacity of the neuron. Then, at that point, the neuron yield vector B is
(3)B=∑jNAjεj+∂.


## 3.1.2. Error Correction Learning (Delta Rule)

AllowBin to be the genuine organization result of neuron I at time n when An is information, and Rin addresses the result of the relating test; then, at that point, the blunder can be composed as
(4)Ein=Rin−Bin.Blunder revision learning is to make the goal work in light ofEin arrive at the base worth, with the goal that the real result of each result neuron in the brain network is genuinely nearest to the result of the example [9]. The most generally utilized objective capacity is the mean square blunder measure, characterized as
(5)Q=E12∑iEi2n,where E is the expectation operator. Because when using Q directly as the objective function, it is necessary to count the characteristics of the whole process. In order to solve this difficulty, we generally replace Q with the instantaneous value ωn of Q at time n, namely,
(6)ωn=12∑iEi2n.Using the steepest gradient descent method, we get(7)ΔTijn=ηnEinAjn,where ηn>0 is the learning step size.The brain network models for which this learning rule is effectively applied are exceptionally broad, for example, the least complex perceptron learning calculation, which is likewise the most crude utilization of brain organizations. The most exemplary application is the back-engendering learning calculation, otherwise called the BP calculation [10].


## 3.1.3. Hebb Learning

When the activation of neurons at both ends of a synapse is synchronized, the strength of the connection should increase, and vice versa, mathematically expressed as(8)ΔTijn=FBin,Ajn,where Bin,Ajn are the states of the neurons at both ends of Tij, respectively, and the most commonly used cases are
(9)ΔTijn=ηBinAjn.Due to the correlation betweenΔT and Bin,Ajn, it is also called the correlation learning rule.There are two kinds of networks that apply this rule: discrete Hopfield network and continuous Hopfield network.


## 3.1.4. Competitive Learning

In the serious learning of the organization, each result unit contends with one another; lastly, only one of the most grounded can be initiated [11]. The principles are addressed by the accompanying recipe:
(10)ΔTn=ηAj−Tjk,IfneuronJcompetitionwins,0,IfneuronJcompetitionfails.At the point when the climate wherein the learning framework is found is steady, hypothetically, the factual attributes of the climate can be learned through administered learning, and these measurable qualities can be recalled by the brain network as experience. Assuming that the climate is nonfixed, the typical managed learning does not can track such changes, and to take care of this issue requires the organization to have a specific versatile capacity [12].


## 3.1.5. Artificial Neural Network Classification

The artificial neural network has strong plasticity, and the arbitrary combination of network structure and activation function can construct different forms of artificial neural network.The artificial neural network classification diagram is shown in Figure3.Figure 3
Artificial neural network classification diagram.
(a)
Forward network structure diagram(b)
Feedback network structure diagram(1) Forward Network. The neurons in the forward network are organized progressively, and every neuron is simply associated with the past layer of neurons. The forward network structure is shown in Figure 3(a) [13]. The training data flows from the input layer through the hidden layer to the output layer and propagates in hierarchical order. Each layer of neurons will only receive input signals from the neurons in the previous layer, and subsequent layers will not bring feedback to the previous layers. The well-known perceptron network and BP network are both types of forward networks.(2) Feedback Network. A feedback network refers to a neural network that contains one or more feedback loops. The feedback network structure is shown in Figure 3(b). In the feedback network, there is a feedback signal between the output layer and the input layer. The output value of the network will become the input value of the network again, so that the data information circulates in the entire network until the training results meet the requirements of the network.(3) There Is a Tutor to Learn the Network. Learning with a tutor means that in network learning, a given target output is required for network training. The neural network does not know the external environment, and the artificially given target output samples are equivalent to “mentors,” which represent the best corresponding effect of neural network training. It adjusts the network parameters according to the target output, so that the network output approaches the termination condition. The BP network used in this paper is a typical tutored learning network [14].(4) Tutorless Learning Network. In contrast to tutored learning networks, tutored learning does not have external tutors or evaluations to view the learning process. Its learning methods are divided into reinforcement learning and self-organizing learning. In reinforcement learning, there is a mapping relationship between the output and the output of the network, and the learning and training of the network are realized according to the performance scalar index that minimizes its continuous interaction with the external environment. In self-organizing learning, a measure of the quality of the network learning representation method is provided, and the network parameters are adjusted according to the provided standard to achieve optimal results.


## 3.2. BP Neural Network and Algorithm

BP (back propagation) brain network is a multifacet forward network with coach learning in light of blunder back engendering calculation preparing. It is made out of numerous neurons associated with one another as indicated by specific standards [15]. The BP brain network model is displayed in Figure 4. BPNN is basically the issue of tackling the base worth of the blunder capacity, and it is a significant and exemplary calculation.Figure 4
Structure of BP network.


### 3.2.1. Basic Structure of BP Neural Network

The basic BP network algorithm contains two propagation directions: the forward propagation of the input signal and the back propagation of the output error. That is to say, when calculating the actual network output, it is carried out in the direction from input to output, but the correction of the weights and thresholds of each layer of the network is carried out in the reverse direction from output to input.The sign of BP brain network is forward conduction. The sign is sent from the info hub to the secret layer hub through weighting and capacity change, and its worth is(11)Dj=Fnetj,j=1,2,⋯,m,(12)netj=∑i=0nUijGi,j=1,2,⋯,m.For the BP network with multihidden layer structure, the signal is transmitted from the hidden layer of the previous layer to the next hidden layer; for the single hidden layer structure shown in the figure, the signal is directly transmitted from the hidden layer to the output layer, as follows:(13)Vk=Fnetk,j=1,2,⋯,k,(14)netk=∑j=0mRjkGj,k=1,2,⋯,s.Among them,FX is called the transfer function, and the unipolar sigmoid function (hyperbolic tangent function) is generally used:
(15)FX=11+E−X.Make it differentiable and continuous, and(16)F′X=FX1−FX.Sometimes, depending on the application needs, a bipolar sigmoid function can also be used:(17)FX=1−E−X1+E−X.


### 3.2.2. BP Learning Algorithm

The core idea of the BP learning algorithm is to repeat the forward conduction and reverse conduction process of the error until the output error meets the design requirements [16].The expected output of the neural network isY=Y1,Y2,⋯,YlT, and the output error e is defined as
(18)e=12Y−L2=12∑k=1sYk−Lk2.Expand the formula layer by layer in reverse. For the hidden layer, there are(19)e=12∑k=1sYk−Fnetk2=12∑k=1sYk−F∑j=0mVjkDj2.Expanding further to the output layer, there are(20)e=12∑k=1sYk−F∑j=0mVjkFnetk2=12∑k=1sYk−F∑j=0mVjkF∑i=0nUijXi2.It can be seen from the formula that the output errore is affected by the connection weights Vjk and Uij between the layers. The purpose of BP learning is to adjust the weight matrices V and U to reduce the output error e to the minimum value. Let the adjustment of the weights be proportional to the gradient descent of the error, namely,
(21)ΔVjk=−η∂e∂vjk,(22)ΔUij=−η∂e∂Uij.In the two formulas, the constantη is the learning rate, which can affect the speed of weight adjustment, which in turn affects the speed of the entire learning process. When η∈0,1 and η are large, it is easier to jump out of the local minimum interval of the output error e, and the learning speed is fast, but the adjustment accuracy is poor [17].Substitute the expression ofE in formulas (19) and (20) into the gradient adjustment formulas (21) and (22), and the weight adjustment formula of the 3-layer BPNN can be obtained:
(23)ΔVjk=ηYk−LkLk1−LkDj,(24)ΔUij=η∑k=1sYk−LkLk1−LkVjkDj1−DjXi.The derivative of the unipolar sigmoid function, formula (16), is applied. For multihidden layer BPNN, it is only necessary to reversely derive the weight adjustment formula according to the rules [18]. The flow chart of the standard BP network program is shown in Figure 5.Figure 5
BP algorithm program flow chart.BP neural network has three typical characteristics: (1) It is made out of numerous layers, and the neurons in nearby layers are completely associated, and the neurons in a similar layer are not associated. (2) The organization preparing process is done by the mistake back engendering calculation. The sign streams from the information layer of the organization through the secret layer to the result layer. When the network weights are trained, the network weights are corrected reversely from the output layer to the input layer in the direction of reducing the error; furthermore, this interaction is rehashed until the mistake will in general be tiny. (3) The enactment capacity of the organization is differentiable; typically, a sigmoid capacity or a direct capacity is utilized. Among them, the sigmoid capacity is isolated into two sorts: log-sigmoid capacity and tan-sigmoid capacity [19].The bends of the log-sigmoid capacity and the tan-sigmoid capacity are displayed in Figures6(a) and 6(b), individually. As can be seen from the figure, the sigmoid function is smooth and differentiable, and it has better fault tolerance than the linear function in classification. The sigmoid function maps the input data from the range of −∞,+∞ to the (-1,1) or (0,1) interval and has the characteristics of nonlinear amplification.Figure 6
Activation function image.
(a)
Log-sigmoid function(b)
Tan-sigmoid function


## 3.2.1. Basic Structure of BP Neural Network

The basic BP network algorithm contains two propagation directions: the forward propagation of the input signal and the back propagation of the output error. That is to say, when calculating the actual network output, it is carried out in the direction from input to output, but the correction of the weights and thresholds of each layer of the network is carried out in the reverse direction from output to input.The sign of BP brain network is forward conduction. The sign is sent from the info hub to the secret layer hub through weighting and capacity change, and its worth is(11)Dj=Fnetj,j=1,2,⋯,m,(12)netj=∑i=0nUijGi,j=1,2,⋯,m.For the BP network with multihidden layer structure, the signal is transmitted from the hidden layer of the previous layer to the next hidden layer; for the single hidden layer structure shown in the figure, the signal is directly transmitted from the hidden layer to the output layer, as follows:(13)Vk=Fnetk,j=1,2,⋯,k,(14)netk=∑j=0mRjkGj,k=1,2,⋯,s.Among them,FX is called the transfer function, and the unipolar sigmoid function (hyperbolic tangent function) is generally used:
(15)FX=11+E−X.Make it differentiable and continuous, and(16)F′X=FX1−FX.Sometimes, depending on the application needs, a bipolar sigmoid function can also be used:(17)FX=1−E−X1+E−X.


## 3.2.2. BP Learning Algorithm

The core idea of the BP learning algorithm is to repeat the forward conduction and reverse conduction process of the error until the output error meets the design requirements [16].The expected output of the neural network isY=Y1,Y2,⋯,YlT, and the output error e is defined as
(18)e=12Y−L2=12∑k=1sYk−Lk2.Expand the formula layer by layer in reverse. For the hidden layer, there are(19)e=12∑k=1sYk−Fnetk2=12∑k=1sYk−F∑j=0mVjkDj2.Expanding further to the output layer, there are(20)e=12∑k=1sYk−F∑j=0mVjkFnetk2=12∑k=1sYk−F∑j=0mVjkF∑i=0nUijXi2.It can be seen from the formula that the output errore is affected by the connection weights Vjk and Uij between the layers. The purpose of BP learning is to adjust the weight matrices V and U to reduce the output error e to the minimum value. Let the adjustment of the weights be proportional to the gradient descent of the error, namely,
(21)ΔVjk=−η∂e∂vjk,(22)ΔUij=−η∂e∂Uij.In the two formulas, the constantη is the learning rate, which can affect the speed of weight adjustment, which in turn affects the speed of the entire learning process. When η∈0,1 and η are large, it is easier to jump out of the local minimum interval of the output error e, and the learning speed is fast, but the adjustment accuracy is poor [17].Substitute the expression ofE in formulas (19) and (20) into the gradient adjustment formulas (21) and (22), and the weight adjustment formula of the 3-layer BPNN can be obtained:
(23)ΔVjk=ηYk−LkLk1−LkDj,(24)ΔUij=η∑k=1sYk−LkLk1−LkVjkDj1−DjXi.The derivative of the unipolar sigmoid function, formula (16), is applied. For multihidden layer BPNN, it is only necessary to reversely derive the weight adjustment formula according to the rules [18]. The flow chart of the standard BP network program is shown in Figure 5.Figure 5
BP algorithm program flow chart.BP neural network has three typical characteristics: (1) It is made out of numerous layers, and the neurons in nearby layers are completely associated, and the neurons in a similar layer are not associated. (2) The organization preparing process is done by the mistake back engendering calculation. The sign streams from the information layer of the organization through the secret layer to the result layer. When the network weights are trained, the network weights are corrected reversely from the output layer to the input layer in the direction of reducing the error; furthermore, this interaction is rehashed until the mistake will in general be tiny. (3) The enactment capacity of the organization is differentiable; typically, a sigmoid capacity or a direct capacity is utilized. Among them, the sigmoid capacity is isolated into two sorts: log-sigmoid capacity and tan-sigmoid capacity [19].The bends of the log-sigmoid capacity and the tan-sigmoid capacity are displayed in Figures6(a) and 6(b), individually. As can be seen from the figure, the sigmoid function is smooth and differentiable, and it has better fault tolerance than the linear function in classification. The sigmoid function maps the input data from the range of −∞,+∞ to the (-1,1) or (0,1) interval and has the characteristics of nonlinear amplification.Figure 6
Activation function image.
(a)
Log-sigmoid function(b)
Tan-sigmoid function


## 3.3. Application of Artificial Neural Network in Innovation and Entrepreneurship Education Evaluation

### 3.3.1. Status

This paper sorts out the literature on teaching quality evaluation of BP neural network in innovation and entrepreneurship education by many scholars and reflects the current development trend. It seeks new directions, grasps new trends, and brings new enlightenment to the research of this paper. To begin with, the BP brain network model utilized in the showing quality assessment of advancement and business instruction has been somewhat full grown, which demonstrates the appropriateness of the strategy and elucidates the comparing finishing joins. Secondly, in order to better promote innovation and entrepreneurship education and carry out in-depth education reform, this paper has laid a good foundation and provided more diverse evaluation and selection methods [20].


### 3.3.2. Defect Analysis and Improvement Demands of the Current Quality Evaluation of Innovation and Entrepreneurship Education

The formulation of innovation and entrepreneurship education should be carried out in accordance with the principles of scientificity, integration, systematicness, and objectivity and fully reflect the natural characteristics and attributes of the object being evaluated. However, there is a problem of blind quantification in the setting of China’s innovation and entrepreneurship evaluation system, and the observation points are not comprehensive, which cannot timely and accurately reflect the evaluation subject, and the evaluation indicators are unclear. The issues have genuinely impacted the assessment nature of advancement and business venture training partially and ruined the improvement of development and business schooling [21].In order to establish a perfect teaching quality evaluation system, we must first solve a problem of unity of opposites: who should evaluate and what is the evaluation content, that is, the choice of evaluation subject and evaluation object. In the traditional teaching quality evaluation system with a single evaluation subject, the evaluation content is not comprehensive, which increases the possibility of distortion of the final evaluation results.As should be visible from Table1, the assessment strategies have clear impediments and can assess one angle. High intricacy and thoroughness are the greatest attributes of development and business instruction, so the relating assessment exercises ought to likewise take on adaptable and different means [22].Table 1
Common evaluation patterns.
Evaluation methodAdvantageShortcomingApplicationStudent evaluationStudents evaluate teachers deeply and comprehensively according to their own learning feelingsStudents’ attitude towards evaluation will affect the evaluation results; students’ own learning level will affect their understanding of teaching objectivesIt is widely used and easy to operateTeacher self-evaluationLet teachers understand the gap with standards and promote teachers’ developmentIt is difficult to determine whether the evaluator is objective in the process of self-evaluationEncouraging teachers’ self-development is an opportunity for teachers to understand themselvesExpert evaluationIn terms of knowledge level and experience, they should far surpass students; the evaluation is serious and responsible with high credibilityIn the process of evaluation, we pay attention to generality and universality, and special links in teaching will be ignored to a certain extent; unable to fully understand teaching activities due to the limited number of lecturesWidely popularized in ChinaManager evaluationBe familiar with the evaluation criteria and be responsible for the evaluation processUnable to fully understand teaching activities due to the limited number of lecturesIt is used for award selection and random inspection of teaching quality


## 3.3.1. Status

This paper sorts out the literature on teaching quality evaluation of BP neural network in innovation and entrepreneurship education by many scholars and reflects the current development trend. It seeks new directions, grasps new trends, and brings new enlightenment to the research of this paper. To begin with, the BP brain network model utilized in the showing quality assessment of advancement and business instruction has been somewhat full grown, which demonstrates the appropriateness of the strategy and elucidates the comparing finishing joins. Secondly, in order to better promote innovation and entrepreneurship education and carry out in-depth education reform, this paper has laid a good foundation and provided more diverse evaluation and selection methods [20].


## 3.3.2. Defect Analysis and Improvement Demands of the Current Quality Evaluation of Innovation and Entrepreneurship Education

The formulation of innovation and entrepreneurship education should be carried out in accordance with the principles of scientificity, integration, systematicness, and objectivity and fully reflect the natural characteristics and attributes of the object being evaluated. However, there is a problem of blind quantification in the setting of China’s innovation and entrepreneurship evaluation system, and the observation points are not comprehensive, which cannot timely and accurately reflect the evaluation subject, and the evaluation indicators are unclear. The issues have genuinely impacted the assessment nature of advancement and business venture training partially and ruined the improvement of development and business schooling [21].In order to establish a perfect teaching quality evaluation system, we must first solve a problem of unity of opposites: who should evaluate and what is the evaluation content, that is, the choice of evaluation subject and evaluation object. In the traditional teaching quality evaluation system with a single evaluation subject, the evaluation content is not comprehensive, which increases the possibility of distortion of the final evaluation results.As should be visible from Table1, the assessment strategies have clear impediments and can assess one angle. High intricacy and thoroughness are the greatest attributes of development and business instruction, so the relating assessment exercises ought to likewise take on adaptable and different means [22].Table 1
Common evaluation patterns.
Evaluation methodAdvantageShortcomingApplicationStudent evaluationStudents evaluate teachers deeply and comprehensively according to their own learning feelingsStudents’ attitude towards evaluation will affect the evaluation results; students’ own learning level will affect their understanding of teaching objectivesIt is widely used and easy to operateTeacher self-evaluationLet teachers understand the gap with standards and promote teachers’ developmentIt is difficult to determine whether the evaluator is objective in the process of self-evaluationEncouraging teachers’ self-development is an opportunity for teachers to understand themselvesExpert evaluationIn terms of knowledge level and experience, they should far surpass students; the evaluation is serious and responsible with high credibilityIn the process of evaluation, we pay attention to generality and universality, and special links in teaching will be ignored to a certain extent; unable to fully understand teaching activities due to the limited number of lecturesWidely popularized in ChinaManager evaluationBe familiar with the evaluation criteria and be responsible for the evaluation processUnable to fully understand teaching activities due to the limited number of lecturesIt is used for award selection and random inspection of teaching quality


## 4. Experiment and Analysis of BP Neural Network on Innovative College Students’ Educational Management Mode

### 4.1. New Problems in the Education of College Students in the New Era

#### 4.1.1. The Only Child Problem

Affected by national policies, most college students are only children at this stage. Because they are cared for by the whole family since childhood, they rarely have contact with the society, resulting in their lack of social awareness. In addition, because there is always family care, most unmarried children lack the spirit of bearing hardships and stand hard work, and when they enter the university, some students will find it difficult to adapt to this situation.


#### 4.1.2. Personality Problems

A university is a small society. Students come from all over the world, some have excellent family conditions, and some have difficult family conditions. In the case of differences in family environment, in addition to the serious psychological problems of college students, some college students with poor family environment are more prone to problems such as inferiority complex and introversion. This is not only good for students’ learning and growth but also can cause serious harm to future career development [23].


#### 4.1.3. Health Problems

Health problems are not only reflected in the physical aspect but also in the psychological aspect of the students. According to relevant research, due to the various pressures they face after entering the university, some students often have psychological burdens, and with the passage of time, more and more psychological problems. Physical health problems are visible, specific, and easy to treat. However, because psychological problems cannot be seen or touched, it is difficult to effectively solve psychological problems.


#### 4.1.4. Employment Issues

As the number of college students increases year by year, the competition for employment is becoming more and more intense. At the same time, many college students are often reluctant to start at the grassroots level in the process of choosing a career and have higher requirements for work and wages, thus increasing the difficulty of finding employment. The reason for this problem is that students do not know enough about the society and cannot accurately locate themselves.The following focuses on the research and analysis of the innovative college students’ educational management model with the employment problem.


### 4.2. Evaluation Application Examples

Business venture instruction is another instructive idea and instructive model and a recent fad in present day schooling. Especially the important content innovation of higher education, through quantitative evaluation, evaluation, and testing, it effectively stimulates organizational innovation. Doing the examination and practice of business venture training is of extraordinary importance for advancing the compelling execution and activity of business venture instruction and further developing the advancement capacity of business venture schooling (Table2).Table 2
Neural network evaluation results.
Evaluation methodSample 1Sample 2Sample 3Sample 4Sample 5Expert evaluation0.6250.7890.8950.9270.936Neural network evaluation0.6270.7930.8930.9250.932Error0.0020.0040.0020.0020.004The information in this article comes from the aftereffects of the advancement and business venture schooling assessment of a school in the 2018-2019 scholarly year. This paper utilizes MATLAB 15.0 programming to lay out the model advances, fabricates the forecast model for the assessment of pioneering development showing quality, and cycles the information. As per the model, the quantity of neurons in the result layer of this paper is 1. It is the assessment consequence of the showing nature of development and business instructing of secondary teachers, which is isolated into four grades: superb (80-100), moderate (70-79), great (80-89), and pass (60-69). The quantity of stowed away layers is 6, the quantity of neurons is 10, the learning rate is 0.2 ~ 0.9, the force factor coefficient is 0.5, and the intermingling blunder edge is set to 0.02.


### 4.3. Utilization of BP Neural Network in the Teaching Quality Evaluation of Tourism Professional Innovation and Entrepreneurship Education in Wenzhou University

In order to verify the quality evaluation index system and evaluation model method of this paper, this study takes Wenzhou University as the research case and the other 6 universities as the sample objects for empirical analysis. This paper evaluates the implementation effect and quality and puts forward suggestions for improvement based on this.From the perspective of result evaluation and process input, this paper constructs a tourism innovation and entrepreneurship education index system with 7 secondary indicators and 14 tertiary indicators after screening according to the expert survey method, as shown in Table3.Table 3
Showing quality evaluation system of innovation and entrepreneurship education in colleges and universities.
Evaluation systemPrimary indexSecondary indexTertiary indicatorsEvaluation index system of innovation and entrepreneurship educationUniversity linksSoft environmentNumber of school enterprise cooperation, A1Number of school enterprise cooperation projects, A2Hardware supportNumber of innovation and entrepreneurship institutions, A3Conversion rate of innovative achievements, A4Teaching linkTeaching methods of curriculum designCore curriculum ratio, A5Participation rate of students in practical courses, A6Participation rate of students in practical coursesInnovation and entrepreneurship ability of academic teamNumber of citations of papers by newspapers and government documents, A7Transformation proportion of innovative achievements, A8Student evaluationStudent backgroundProportion of students who have participated in entrepreneurship courses, A9Proportion of part-time students, A10Student performanceIncrease rate of students’ innovative achievements, A11Examination results of students’ innovation and entrepreneurship knowledge, A12Innovation and entrepreneurship competition, A13Student satisfactionAttendance rate of innovation and entrepreneurship courses, A14Utilizing the Wenzhou University information gathered by the poll review to assess the exhibition of development and business schooling and applying the prepared and further developed BP brain network model, the exhaustive organization yield esteem is 0.7261. It demonstrates that the advancement and business schooling of Wenzhou University is at a medium level, and there is still a lot of opportunity to get better.It can be seen from Figure7 that A1=0.83, A2=0.97, A5=0.97, A11=0.91, and A12=0.89 by analyzing the evaluation results of the indicators. It can be known that the teachers of University X have paid attention to encouraging students to start businesses in the field of tourism while teaching tourism courses. Students have gradually begun to master the relevant knowledge and began to use other disciplines and the Internet to discuss the new tourism professional entrepreneurial model. And they pay attention to practical teaching while imparting professional theoretical knowledge. Combined with the characteristics of tourism professional disciplines, they have continued to improve from discipline design, internship positions, and after-school experiments. Through continuous efforts, the number of students participating in innovation and entrepreneurship special courses has been increasing, and they are highly satisfied with the teaching effect. It gives a relating assurance to the proceeded with advancement of development and business training and has slowly expanded and extended in school-endeavor participation.Figure 7
Output results of each indicator.
(a)(b)As per the assessment pointersX24=0.80, A9=0.90, A13=0.90, and A14=0.91, it tends to be realized that Wenzhou University has gained incredible headway in the scholarly classes on development and business venture instruction for the travel industry studies late years. Its research results are gradually increasing in the number of related journals, the social practice results are remarkable, the proportion of awards in academic competitions in related fields is increasing, and its social influence is increasing. By implementing a corresponding reward mechanism for award-winning teachers and students, more academics are encouraged to invest in research in related fields.


## 4.1. New Problems in the Education of College Students in the New Era

### 4.1.1. The Only Child Problem

Affected by national policies, most college students are only children at this stage. Because they are cared for by the whole family since childhood, they rarely have contact with the society, resulting in their lack of social awareness. In addition, because there is always family care, most unmarried children lack the spirit of bearing hardships and stand hard work, and when they enter the university, some students will find it difficult to adapt to this situation.


### 4.1.2. Personality Problems

A university is a small society. Students come from all over the world, some have excellent family conditions, and some have difficult family conditions. In the case of differences in family environment, in addition to the serious psychological problems of college students, some college students with poor family environment are more prone to problems such as inferiority complex and introversion. This is not only good for students’ learning and growth but also can cause serious harm to future career development [23].


### 4.1.3. Health Problems

Health problems are not only reflected in the physical aspect but also in the psychological aspect of the students. According to relevant research, due to the various pressures they face after entering the university, some students often have psychological burdens, and with the passage of time, more and more psychological problems. Physical health problems are visible, specific, and easy to treat. However, because psychological problems cannot be seen or touched, it is difficult to effectively solve psychological problems.


### 4.1.4. Employment Issues

As the number of college students increases year by year, the competition for employment is becoming more and more intense. At the same time, many college students are often reluctant to start at the grassroots level in the process of choosing a career and have higher requirements for work and wages, thus increasing the difficulty of finding employment. The reason for this problem is that students do not know enough about the society and cannot accurately locate themselves.The following focuses on the research and analysis of the innovative college students’ educational management model with the employment problem.


## 4.1.1. The Only Child Problem

Affected by national policies, most college students are only children at this stage. Because they are cared for by the whole family since childhood, they rarely have contact with the society, resulting in their lack of social awareness. In addition, because there is always family care, most unmarried children lack the spirit of bearing hardships and stand hard work, and when they enter the university, some students will find it difficult to adapt to this situation.


## 4.1.2. Personality Problems

A university is a small society. Students come from all over the world, some have excellent family conditions, and some have difficult family conditions. In the case of differences in family environment, in addition to the serious psychological problems of college students, some college students with poor family environment are more prone to problems such as inferiority complex and introversion. This is not only good for students’ learning and growth but also can cause serious harm to future career development [23].


## 4.1.3. Health Problems

Health problems are not only reflected in the physical aspect but also in the psychological aspect of the students. According to relevant research, due to the various pressures they face after entering the university, some students often have psychological burdens, and with the passage of time, more and more psychological problems. Physical health problems are visible, specific, and easy to treat. However, because psychological problems cannot be seen or touched, it is difficult to effectively solve psychological problems.


## 4.1.4. Employment Issues

As the number of college students increases year by year, the competition for employment is becoming more and more intense. At the same time, many college students are often reluctant to start at the grassroots level in the process of choosing a career and have higher requirements for work and wages, thus increasing the difficulty of finding employment. The reason for this problem is that students do not know enough about the society and cannot accurately locate themselves.The following focuses on the research and analysis of the innovative college students’ educational management model with the employment problem.


## 4.2. Evaluation Application Examples

Business venture instruction is another instructive idea and instructive model and a recent fad in present day schooling. Especially the important content innovation of higher education, through quantitative evaluation, evaluation, and testing, it effectively stimulates organizational innovation. Doing the examination and practice of business venture training is of extraordinary importance for advancing the compelling execution and activity of business venture instruction and further developing the advancement capacity of business venture schooling (Table2).Table 2
Neural network evaluation results.
Evaluation methodSample 1Sample 2Sample 3Sample 4Sample 5Expert evaluation0.6250.7890.8950.9270.936Neural network evaluation0.6270.7930.8930.9250.932Error0.0020.0040.0020.0020.004The information in this article comes from the aftereffects of the advancement and business venture schooling assessment of a school in the 2018-2019 scholarly year. This paper utilizes MATLAB 15.0 programming to lay out the model advances, fabricates the forecast model for the assessment of pioneering development showing quality, and cycles the information. As per the model, the quantity of neurons in the result layer of this paper is 1. It is the assessment consequence of the showing nature of development and business instructing of secondary teachers, which is isolated into four grades: superb (80-100), moderate (70-79), great (80-89), and pass (60-69). The quantity of stowed away layers is 6, the quantity of neurons is 10, the learning rate is 0.2 ~ 0.9, the force factor coefficient is 0.5, and the intermingling blunder edge is set to 0.02.


## 4.3. Utilization of BP Neural Network in the Teaching Quality Evaluation of Tourism Professional Innovation and Entrepreneurship Education in Wenzhou University

In order to verify the quality evaluation index system and evaluation model method of this paper, this study takes Wenzhou University as the research case and the other 6 universities as the sample objects for empirical analysis. This paper evaluates the implementation effect and quality and puts forward suggestions for improvement based on this.From the perspective of result evaluation and process input, this paper constructs a tourism innovation and entrepreneurship education index system with 7 secondary indicators and 14 tertiary indicators after screening according to the expert survey method, as shown in Table3.Table 3
Showing quality evaluation system of innovation and entrepreneurship education in colleges and universities.
Evaluation systemPrimary indexSecondary indexTertiary indicatorsEvaluation index system of innovation and entrepreneurship educationUniversity linksSoft environmentNumber of school enterprise cooperation, A1Number of school enterprise cooperation projects, A2Hardware supportNumber of innovation and entrepreneurship institutions, A3Conversion rate of innovative achievements, A4Teaching linkTeaching methods of curriculum designCore curriculum ratio, A5Participation rate of students in practical courses, A6Participation rate of students in practical coursesInnovation and entrepreneurship ability of academic teamNumber of citations of papers by newspapers and government documents, A7Transformation proportion of innovative achievements, A8Student evaluationStudent backgroundProportion of students who have participated in entrepreneurship courses, A9Proportion of part-time students, A10Student performanceIncrease rate of students’ innovative achievements, A11Examination results of students’ innovation and entrepreneurship knowledge, A12Innovation and entrepreneurship competition, A13Student satisfactionAttendance rate of innovation and entrepreneurship courses, A14Utilizing the Wenzhou University information gathered by the poll review to assess the exhibition of development and business schooling and applying the prepared and further developed BP brain network model, the exhaustive organization yield esteem is 0.7261. It demonstrates that the advancement and business schooling of Wenzhou University is at a medium level, and there is still a lot of opportunity to get better.It can be seen from Figure7 that A1=0.83, A2=0.97, A5=0.97, A11=0.91, and A12=0.89 by analyzing the evaluation results of the indicators. It can be known that the teachers of University X have paid attention to encouraging students to start businesses in the field of tourism while teaching tourism courses. Students have gradually begun to master the relevant knowledge and began to use other disciplines and the Internet to discuss the new tourism professional entrepreneurial model. And they pay attention to practical teaching while imparting professional theoretical knowledge. Combined with the characteristics of tourism professional disciplines, they have continued to improve from discipline design, internship positions, and after-school experiments. Through continuous efforts, the number of students participating in innovation and entrepreneurship special courses has been increasing, and they are highly satisfied with the teaching effect. It gives a relating assurance to the proceeded with advancement of development and business training and has slowly expanded and extended in school-endeavor participation.Figure 7
Output results of each indicator.
(a)(b)As per the assessment pointersX24=0.80, A9=0.90, A13=0.90, and A14=0.91, it tends to be realized that Wenzhou University has gained incredible headway in the scholarly classes on development and business venture instruction for the travel industry studies late years. Its research results are gradually increasing in the number of related journals, the social practice results are remarkable, the proportion of awards in academic competitions in related fields is increasing, and its social influence is increasing. By implementing a corresponding reward mechanism for award-winning teachers and students, more academics are encouraged to invest in research in related fields.


## 5. Discussion

As of late, the historical backdrop of school training the board has both new authentic experience and significant recorded examples. As a general rule, the instructive administration of undergrads puts an excessive amount of accentuation on philosophy, with the goal that the mission of instructive administration of understudies is consistently to serve and submit to the focal work of communist upheaval and development. It emphasizes the function of service and obedience more from the national and social standards, pays attention to the exertion of instrumental value, and forms a narrow value that is dominated by instrumental rationality. Under the leadership of this kind of values, the educational management of college students has made obvious achievements, but the development process is also quite tortuous.All things considered, the social necessities of undergrads’ instructive administration are the outside elements of understudies’ instructive administration. The intrinsic value of college students’ education management is more worthy of attention to the improvement of people and human nature. Hence, underscoring the instrumental judiciousness of the instructive administration of undergrads, the removal of the worth of individual improvement is loathed, bringing about the inconsistency between the outside friendly necessities and the interior human requirements. The instructive administration of understudies is by all accounts the outside prerequisites of undergrads, as opposed to the necessities of their own turn of events, bringing about “nobody” in the schooling the executives of undergrads. The education management of college students with “no one” in their hearts obscures the more important “purpose rationality” of shaping a sound personality, cultivating a good citizen, and promoting the free and all-round development of people. It may result in “sowing dragon seeds, harvesting fleas.”Because of the various leveled and arranged nature of hypothetical instructive objectives, the division responsible for the instructive administration of undergrads is additionally obviously mindful that the instructive administration of understudies heavily influenced by instrumental worth is just appropriate for explicit circumstances and conditions. Therefore, blindly pursuing the tool value of college education management at any time will inevitably dilute the meaning of humanistic value. During this period, training the board of understudies sets forward the standard of sticking to individuals situated, near the real world, near life, and near understudies.


## 6. Conclusion

This paper is aimed at enriching the theory and method of entrepreneurship education evaluation in colleges and universities. Accepting Wenzhou University as the examination test, this paper lays out the significant assessment record framework and develops the assessment model of business schooling in schools and colleges. Business venture schooling in view of BP brain organization, this paper gives a premise to the assessment and preparing of business instruction in schools and colleges. This paper grows the hypothesis and strategy for creative business venture training assessment and investigates and chooses imaginative business venture schooling assessment markers. Joined with China’s inventive business model, this paper proposes another assessment point. To summarize, with the nonstop developing of instructive change, the conventional instructive administration model can presently not meet the prerequisites of the general public in the new time, and schools and colleges should perceive their own concerns. At present, college students and educators strictly follow certain principles in the actual work process. On the basis of continuous innovation of education management mode, we can continuously deliver the talents to the society.



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*Source: 7503315-2022-07-20.xml*