# The Analysis of Sharing Economy on New Business Model Based on BP Neural Network

Authors: Gang Cai; Chunmei Ni
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

The development of social economy and Internet information technology has made the development of the sharing economy relatively rapid. This article aims to study how to promote the sharing economy based on neural networks to play a role in new business models. This article proposes that the sharing economy and the new business model are inseparable. It also discusses how to analyze the relationship between the sharing economy and the new business model based on the BP neural network. With the development of the economy and society, new economic development models have developed, and the sharing economy model has risen. The sharing economy model has brought an impact to the traditional economic development model, affecting the business model. The results show that with the development of society and enterprises, the development of the sharing economy is getting faster and faster. Today, some sharing economy companies are bound to face various obstacles in the process of copying other business models and development. Sharing economy enterprises have made various adjustments and responses to various problems, but they have not found a better model to adapt to the modern social market and environment. Therefore, the business model of the sharing economy requires further analysis and investigation.


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## Body

## 1. Introduction

With the development of society, the sharing economy has become a new business model. It can be called common consumption or sharing economy, and its central idea is to share idle resources. Because of the low cost of sharing, it has more advantages than the previous business model. For example, the sharing economy makes full use of idle resources; the sharing economy can reduce transaction costs; the sharing economy can establish a personal brand, and so on. In this article, the battle for business models derived from the sharing economy is getting worse. The sharing economy model brought about by technological revolutions in Internet technology, industry, and communications is gradually eroding the traditional business chain. The sharing economy business model represented by the Internet economy is a traditional economic business model. This is not a simple substitute but is playing an active role.With the development of mobile Internet technology and the combination of big data, cloud computing, smart terminals, and other technologies, a new business model of “sharing economy” has emerged. The sharing economy will not only affect various industries but also have a huge impact on people’s living habits and actions. The business model of the sharing economy must also be affected by technological changes. Technological pioneers will also gain a first-mover advantage in the market and gain the benefits of technological advancement.With the rapid development of Internet technology, the sharing economy has been brought about. Guyader H. has raised interest in the rise of the sharing economy by clarifying current trends in the mobile field and new companies operating different business models at the same time. Taking the shared travel platform as an example, study the underexplored business model portfolio diversification process and verify that the successful business model configuration can maximize the use of the company’s existing resources, to establish a sustainable competitive advantage that is difficult to imitate and create theoretical propositions. Data collection was conducted through interviews and document analysis with key insiders of the platform management team. This analysis shows the evolution, diversification, and expansion of sharing economy startups from nonprofit carpooling sites to for-profit matching platforms that provide peer-to-peer mobile solutions [1]. Lombardi P researched, developed, and analyzed a business model based on the principles of the sharing economy. Under this model, energy storage operators provide their energy storage systems to different types of customers. Each customer uses ESS for their single-use case, and a different set of use cases has been identified to make the operation of ESS profitable. Different types of stationary batteries are considered energy storage technologies, and they are different in terms of investment costs and technical characteristics. The developed business model simulation shows that compared with a single use case business model, a model based on the sharing economy may improve the profitability of operating battery storage systems. In addition, it was found that a larger battery size in terms of power and capacity is profitable and leads to an increase in revenue stream [2]. Maria A. A. found that in the past decade, the sharing economy has become a business model that improves the use of goods, uses fewer resources than traditional markets, increases social interaction, and promotes more responsible and environmentally friendly consumption. This has led many authors to propose that the sharing economy may be a business model that will change the relationship between consumers and goods and their material lifestyles. This exploratory study is the first to specifically identify new consumer materialism in the sharing economy. To this end, a survey of 384 participants in the sharing economy was conducted, not only to determine the driving factors of the new materialism but also to determine their impact on consumption. The conclusion shows that new materialism is currently being experienced, in which the main elements of traditional materialism—the accumulation of property and commodities and the happiness obtained from the accumulation of commodities and the display as a status symbol— are losing importance [3]. Geissinger A has established a new platform with a digital intermediary and peer-to-peer communication as the core of its business model, and the sharing economy has gained appeal in multiple industries. Most of the research on the sharing economy focuses on the phenomenon level or the operation of a single platform. By asking how the sharing economy is spread to the new platform, his purpose is to explain the communication mode of the sharing economy business model. The survey results point to a seamless and unobtrusive model that echoes the characteristics of a shared economy business model that spans remote sectors to avoid competition while replicating activities in a constantly changing resource environment. In addition, he continues to explore the sharing economy related to industrial marketing by shifting from a single platform to the way they lead to a new platform while acknowledging the innovative model of the new platform [4]. Ciulli found that in addition to promoting the rise of new players in various fields, the sharing economy has also attracted the attention of established companies, the so-called “established companies.” Some established companies join the sharing economy not only to reap emerging opportunities but also to deal with the competition of newcomers. The entry of existing companies is at the initial stage of the sharing economy and is a “battlefield” among participants to defend their original sustainability commitments. Communities are based on the efficient use of resources, social connections, nonmonetary relationships, power, and those who support those who need to compromise in principle to ensure the expansion of the sharing economy. Given the size and power of existing companies, their entry may significantly affect the shape of the sharing economy. Ciulli’s research explored the impact of different ways in which existing companies changed their existing business models to join the sharing economy on the creation of environmental, social, and economic values and developed a shared business model innovation type [5]. Hans discovered the positive impact of new “sustainable” business models, such as the sharing economy business model, which is well documented and they are all well received. However, these sustainable models also have unexpected negative effects. These effects are not obvious and are often overlooked. Hans described these unexpected side effects as a sustainability paradox and will discuss their negative externalities by comparing the characteristics of the sharing economy business model with the sustainable economic model. In addition, a short checklist or framework will be proposed to quickly identify sustainable business models. This framework can promote the top-down implementation of legislative measures and bottom-up prevention of negative externalities of sharing economy measures [6]. Ma Y. found that most shared travel business models promise to achieve green and affordable transportation in the city. However, their rapid expansion process often causes major damage and pressure on urban governance. Free-floating bike sharing is highly touted as a way to bring cycling habits back to cities with overcrowded cars. Although the behavior of citizens using shared bicycles has changed significantly in a short period of time, FFBS has encountered problems of oversupply, insufficient distribution, and improper user behavior, which endanger the environmental and social sustainability of innovation. Ma Y focuses on FFBS case studies and studies from the perspective of collaborative governance how commercial, political, and social actors interact in solving public problems that arise during the scale of FFBS. Ma Y. found that the lack of recognition and integration of new social participants as agents in the plan is a key obstacle to a fully functioning social collaboration system [7]. Clauss T. found that platform-based business models have become an important pillar of today’s economy, so the term “sharing economy” is now often used to describe the new status quo. The success of platforms such as Blablacar or Shpock proves the huge shift from an ownership society to a shared society. However, only a few platforms can accumulate a large number of loyal customers. When success depends on participation, a large user base can make or break one of these platforms. In the research, this challenge is addressed by studying platform loyalty from a customer-centric perspective. It demonstrates the impact of different value perceptions on customer-based platforms through the use of variance-based structural formula models. By showing how customers’ perception of emotional value and quality value promotes platform loyalty, the results can prove that factors other than price determine the loyalty use of platform-based business models [8]. Through the analysis of scholars, it can know that the sharing economy is becoming more and more popular with people. The sharing economy and new business models are complementary to each other. How to promote the development of sharing economy and new business models based on neural networks is what needs the most attention.The innovation of this article is as follows: (1) Based on the investigation and research of BP neural network on the development of modern sharing economy and business through investigation and research method, this article studies the effect of BP neural network on the development of sharing economy and new business models and how to develop the sharing economy sustainably and healthily. (2) This paper also calculates the error of the sharing economy through the algorithm of BP neural network, thereby simulating the new business model and experimenting and analyzing the new business model through the questionnaire survey method.


## 2. BP Neural Network Algorithm

### 2.1. The Basic Model of Sharing Economy and the Basic Concepts of BP Neural Network

The business model, as an empowering tool for small and medium-sized enterprises, determines the future profitability and development direction of enterprises. The new business model combines the sharing economy and discards the traditional bad model. The time-sharing lease and platform model are the basic model of the sharing economy and the earliest C2C model, where the supply and demand side and the demand side are integrated [9]. With the existence of the sharing economy platform, the supply and demand side and the demand side no longer rely on institutions and organizations outside the platform to achieve direct transactions and exchange resources. Both parties to the transaction achieve their goals and achieve their needs. The sharing economy platform charges according to the transaction and can be divided into the following: a, unilateral or bilateral charges; b, proportional or fixed amount charges; c, fixed or floating proportion charges, etc. [10], as shown in Figure 1.Figure 1
The basic model of the sharing economy.As shown in Figure1, the sharing economy has changed people’s traditional way of thinking and has penetrated into all aspects of social production and life. However, in the context of the sharing economy, the clear and typical business operation model of enterprises is still unknown [11]. Now, the detailed research on the sharing economy is mainly carried out in the following aspects: the first is to analyze the cases of typical industries or fields to promote individual research on the overall sharing economy field; the second is the research on the sharing economy and economic benefits in society; the third is the research on the development norms and compliance of the sharing economy [12].With the development of artificial neural network technology, its use is becoming more and more extensive, the application field is also expanding, and its potential is becoming more and more obvious. One of the most prominent features of the BP neural network is to train the BP neural network through a certain number of samples, and the neural network can obtain and store a large number of “input-output” nonlinear mapping relationships [13]. One of the difficulties in supplier selection is that it is difficult to determine the weight assignment of supplier evaluation indicators. The nonlinear mapping ability of the BP neural network can get the mapping output without knowing the index weight, which just solves the problem of difficulty in setting the index weight for supplier selection [14], as shown in Figure 2:Figure 2
Neural network structure diagram.The learning process of the BP neural network in Figure2 consists of two parts: forward propagation and backpropagation. There are two types of cyclic signals in these two propagation processes: one is the sending signal in action. After applying the input signal, the signal propagates forward before the actual output is generated at the output terminal; the other is the wrong sending signal. The actual output of the network is as expected, and the difference between the outputs is wrong. Errors start from the output and propagate back through each layer [15]. When information is propagated in order, the information starts from the input layer, is processed by the hidden unit layer, and then passed to the output layer. The state of each layer of neurons will affect the state of the next layer [16].The learning algorithm of the BP neural network is the gradient descent algorithm. As we all know, the gradient descent algorithm is a very widely used optimization algorithm in machine learning, and it is also the most commonly used optimization method in many machine learning algorithms. The main idea of this algorithm is to minimize the error by modifying the weight. The specific calculation steps are as follows:(1)
Calculate the input valueGj of the hidden layer as follows:(1)Gj=∑i=1nwijxi−θj,j=1,2,...,q.(i)
Among them,wij represents the weight between the ith neuron in the input layer and the jth neuron in the hidden layer; θj represents the threshold of the jth neuron in the hidden layer.(2)
Use the Sigmoid function to calculate the outputxj of the hidden layer neuron as follows:(2)xj=fFj=11+e−Fj,j=1,2,…,q.(3)
Calculate the input valuexr of the output layer neuron as follows:(3)xr=∑r=1qwjryj−θr,r=1,2,…,m.(i)
wherewjr represents the weight between the jth neuron in the hidden layer and the rth neuron in the output layer and represents the threshold of the rth neuron in the output layer.(4)
Use the Sigmoid function to calculate the actual outputxr of the output layer neuron as follows:(4)xr=fFr=11+e−Fr,r=1,2,…,m.(5)
Forward feedback of error value(6)
Errorer is the difference between expected output XR and actual output xr, as follows:(5)er=12∑riXR−xr,r=1,2,…,m.(7)
Using the gradient fast descent method, the weight and the threshold value of the negative gradient alonger are corrected as follows:(6)Δwjr=−η∂er∂wjr−η.The weights and thresholds are repeatedly revised through the above calculation formula until the error is less than the set value and the BP neural network model is built. Based on the forward feedback of the above deviation value, the correction of the weight threshold is forwarded, and the learning process of repeated correction until the deviation is less than the set deviation range, just to avoid the subjectivity of manually assigning weights and can well reproduce expert experience knowledge and thinking [17]. This is also the theoretical algorithm basis for choosing BP neural network to establish the supplier selection model in this article.


### 2.2. Neural Network back Propagation Algorithm

The backpropagation algorithm can be said to be the most basic and important knowledge point of the neural network. Basically, all optimization algorithms are improved after the gradient is calculated by backpropagation. At the same time, because the backpropagation algorithm is a recursive form, the error can be propagated backward layer by layer, which is easy to implement. The BP neural network is composed of an input layer, several hidden layers, and an output layer. The default network model contains only one hidden layer, as shown in Figure3 [18]:Figure 3
Neural network topology structure diagram.As shown in Figure3, the characteristics of this neural network model are as follows: each layer contains one or more neurons, and the neurons of two adjacent layers are connected with adjustable weights. There is no connection between neurons in each layer, and the input and output of the network are highly nonlinear mapping relations [19]. By adjusting the weights, thresholds, and the number of hidden layer nodes of the neural network connection, problems such as nonlinear classification can be realized, and any nonlinear function can be approximated with arbitrary precision [20].Signal A is input from the input layer, enters the hidden layer, and is processed by each node in the hidden layer to obtain B, enters the output layer, and is processed by the output layer nodes to obtain the network output resultD, as shown in Figure 4:Figure 4
Signal flow of BP algorithm.As shown in Figure4: in this stage, the input samples are passed from the input layer, each layer is processed through each hidden layer and then passed to the output layer. If the actual output of the output layer does not match the expected output, enter the second stage, the background attribute stage [21–24].The core idea of the algorithm is to divide the learning process into the following two stages.


#### 2.2.1. Forward Propagation Stage

A forward propagation algorithm, as the name suggests, is an algorithm that proceeds from front to back. At the beginning of the introduction, initialize a set of values and select an activation function, compare the gap between the output result and the expected value. If it does not meet the requirements, the error is propagated back in the network. In the propagation, the error is allocated to each node of each layer so as to adjust the weight of each layer.The output of the first neuron in the hidden layer at this stage is as follows:(7)xf=f∑i=0nvijxi,j=1,2,…,m.The output of the nth neuron in the output layer is as follows:(8)Dn=f∑j=0mwjkxf,n=1,2,…,k.The error function is as follows:(9)E=12∑k=1tdn−on.


#### 2.2.2. Back Propagation Stage

At this stage, the error is sent to the input layer through the hidden layer, and the network allocates errors to the units of each layer to obtain the error signal of the units of each layer. On this basis, the number of units is corrected. The mathematical expression of this stage is as follows:(10)Δyjk=−η∂E∂yjk=ηdn−onon1−onxi.The weight of the output layer is adjusted to the following formula:(11)ΔVjk=−η∂E∂Vjk=ηdn−onon1−xnyi.


### 2.3. Nguyen-Widrow Method

There are the randomization method and the Nguyen-Widrow method for initializing weights and thresholds. In order to solve the problem of slow convergence speed in the application of the neural network in the application of the dissolved gas analysis transformer fault diagnosis in the oil, the Nguyen-Wid-row method is used to initialize the variable parameters of the neural network. Normally, the calculation results show that the randomization method does not seem to be very suitable. In the face of more complex nonlinear systems, the randomization method will lead to low computational efficiency [25]. However, the initialization weight and threshold method proposed by Nguyen and Widrow can achieve the effect of significantly improving the calculation efficiency. In this way, the initial weight Q is as follows:(12)Q1=0.5×mn×normr2randm,n−Im,n.And the initialization thresholda is as follows:(13)a1=0.5×mn×2randm,n−Im,n.If the sample is not processed, it is directly used as the input sample data for BP neural network learning and training because the data unit is inconsistent, which will cause a greater impact on the range of the transfer function. It may cause the algorithm to oscillate and not converge, so it is necessary to standardize the samples, normr(Q) is the standardized normalization matrix of matrix Q: if Q=a1a2b1b2, then:(14)normrQ=a1a1+a1a2a2+a2b1b1+b1b2b2+b2.The initial weights and thresholds of neurons in each layer are random numbers between 0 and 1. The graph of tansig is shown in Figure5:Figure 5
Graph of tansig function.As shown in Figure5: BP network has excellent nonlinear function approximation ability. BP network with only one hidden layer can realize this function. The introduction of multiple hidden layers will speed up the training speed. Experiments show that for the problem to be solved in this article, even in the case of only one hidden layer, the training speed of the BP network can be satisfactory.The error between the output layer and the hidden layer is calculated as follows:(15)ej=qj−oj,where ej is the actual output of the jth neuron in the output layer and oj is the expected output. The error of calculating the hidden layer neuron is as follows:(16)ek=∑j=1iejwjk.The corrected value for calculating the weight of the output layer is as follows:(17)Δek=lrdjyk.In the formula,lr is the learning rate, dj is the local gradient, and yk is the hidden layer output. In the same way, the correction amount for calculating the hidden layer weight is as follows:(18)Δwki=lrdixk.The threshold correction value of the output layer and the hidden layer is as follows:(19)ΔaJ=lrej.If the error accuracy requirement is met, it is as follows:(20)Yi=Yi−YmaxYmax−Ymin.In the neural network, 10 sets of data are used as training samples, and the 11th sample data is used for prediction. Before the error reaches the target error requirement or reaches the set number of cycles, input training samples for learning and training in the BP neural network model. The error curve after learning is shown in Figure6:Figure 6
Error graph.As shown in Figure6: although the accuracy of the empirical prediction results is high, it should be noted that the premise of the prediction is to ensure that the original sample data is true, reasonable, and effective. Otherwise, no matter how high the accuracy is, the results will be meaningless. For determining whether the weight of the predictive index will affect the accuracy, it can be verified by the BP neural network prediction directly through the uncertainty of the index weight.In order to better record the credit credibility of participants in the sharing economy activities, the membership system of the sharing economy was born. Especially in the prevalence of the P2P business model, more and more transparent and true credit management system is gradually formed. The sharing economy business model has derived a variety of membership models, but the social credit mechanism of virtual social capital has actually produced constraints and incentives. Let the credit system of sharing economy participants bind users through members, generate stable user stickiness, gradually improve the credit investigation system, and reduce the credit cost of sharing economy transactions [26, 27]. The membership model of the sharing economy is shown in Figure 7.Figure 7
Membership model diagram.As shown in Figure7, different types of sharing economy business models rely on different relationship structures to produce corresponding changes. This relationship is not static, and it changes differently under certain conditions and circumstances. This change, in turn, will also affect the resources and models of the sharing economy platform.


## 2.1. The Basic Model of Sharing Economy and the Basic Concepts of BP Neural Network

The business model, as an empowering tool for small and medium-sized enterprises, determines the future profitability and development direction of enterprises. The new business model combines the sharing economy and discards the traditional bad model. The time-sharing lease and platform model are the basic model of the sharing economy and the earliest C2C model, where the supply and demand side and the demand side are integrated [9]. With the existence of the sharing economy platform, the supply and demand side and the demand side no longer rely on institutions and organizations outside the platform to achieve direct transactions and exchange resources. Both parties to the transaction achieve their goals and achieve their needs. The sharing economy platform charges according to the transaction and can be divided into the following: a, unilateral or bilateral charges; b, proportional or fixed amount charges; c, fixed or floating proportion charges, etc. [10], as shown in Figure 1.Figure 1
The basic model of the sharing economy.As shown in Figure1, the sharing economy has changed people’s traditional way of thinking and has penetrated into all aspects of social production and life. However, in the context of the sharing economy, the clear and typical business operation model of enterprises is still unknown [11]. Now, the detailed research on the sharing economy is mainly carried out in the following aspects: the first is to analyze the cases of typical industries or fields to promote individual research on the overall sharing economy field; the second is the research on the sharing economy and economic benefits in society; the third is the research on the development norms and compliance of the sharing economy [12].With the development of artificial neural network technology, its use is becoming more and more extensive, the application field is also expanding, and its potential is becoming more and more obvious. One of the most prominent features of the BP neural network is to train the BP neural network through a certain number of samples, and the neural network can obtain and store a large number of “input-output” nonlinear mapping relationships [13]. One of the difficulties in supplier selection is that it is difficult to determine the weight assignment of supplier evaluation indicators. The nonlinear mapping ability of the BP neural network can get the mapping output without knowing the index weight, which just solves the problem of difficulty in setting the index weight for supplier selection [14], as shown in Figure 2:Figure 2
Neural network structure diagram.The learning process of the BP neural network in Figure2 consists of two parts: forward propagation and backpropagation. There are two types of cyclic signals in these two propagation processes: one is the sending signal in action. After applying the input signal, the signal propagates forward before the actual output is generated at the output terminal; the other is the wrong sending signal. The actual output of the network is as expected, and the difference between the outputs is wrong. Errors start from the output and propagate back through each layer [15]. When information is propagated in order, the information starts from the input layer, is processed by the hidden unit layer, and then passed to the output layer. The state of each layer of neurons will affect the state of the next layer [16].The learning algorithm of the BP neural network is the gradient descent algorithm. As we all know, the gradient descent algorithm is a very widely used optimization algorithm in machine learning, and it is also the most commonly used optimization method in many machine learning algorithms. The main idea of this algorithm is to minimize the error by modifying the weight. The specific calculation steps are as follows:(1)
Calculate the input valueGj of the hidden layer as follows:(1)Gj=∑i=1nwijxi−θj,j=1,2,...,q.(i)
Among them,wij represents the weight between the ith neuron in the input layer and the jth neuron in the hidden layer; θj represents the threshold of the jth neuron in the hidden layer.(2)
Use the Sigmoid function to calculate the outputxj of the hidden layer neuron as follows:(2)xj=fFj=11+e−Fj,j=1,2,…,q.(3)
Calculate the input valuexr of the output layer neuron as follows:(3)xr=∑r=1qwjryj−θr,r=1,2,…,m.(i)
wherewjr represents the weight between the jth neuron in the hidden layer and the rth neuron in the output layer and represents the threshold of the rth neuron in the output layer.(4)
Use the Sigmoid function to calculate the actual outputxr of the output layer neuron as follows:(4)xr=fFr=11+e−Fr,r=1,2,…,m.(5)
Forward feedback of error value(6)
Errorer is the difference between expected output XR and actual output xr, as follows:(5)er=12∑riXR−xr,r=1,2,…,m.(7)
Using the gradient fast descent method, the weight and the threshold value of the negative gradient alonger are corrected as follows:(6)Δwjr=−η∂er∂wjr−η.The weights and thresholds are repeatedly revised through the above calculation formula until the error is less than the set value and the BP neural network model is built. Based on the forward feedback of the above deviation value, the correction of the weight threshold is forwarded, and the learning process of repeated correction until the deviation is less than the set deviation range, just to avoid the subjectivity of manually assigning weights and can well reproduce expert experience knowledge and thinking [17]. This is also the theoretical algorithm basis for choosing BP neural network to establish the supplier selection model in this article.


## 2.2. Neural Network back Propagation Algorithm

The backpropagation algorithm can be said to be the most basic and important knowledge point of the neural network. Basically, all optimization algorithms are improved after the gradient is calculated by backpropagation. At the same time, because the backpropagation algorithm is a recursive form, the error can be propagated backward layer by layer, which is easy to implement. The BP neural network is composed of an input layer, several hidden layers, and an output layer. The default network model contains only one hidden layer, as shown in Figure3 [18]:Figure 3
Neural network topology structure diagram.As shown in Figure3, the characteristics of this neural network model are as follows: each layer contains one or more neurons, and the neurons of two adjacent layers are connected with adjustable weights. There is no connection between neurons in each layer, and the input and output of the network are highly nonlinear mapping relations [19]. By adjusting the weights, thresholds, and the number of hidden layer nodes of the neural network connection, problems such as nonlinear classification can be realized, and any nonlinear function can be approximated with arbitrary precision [20].Signal A is input from the input layer, enters the hidden layer, and is processed by each node in the hidden layer to obtain B, enters the output layer, and is processed by the output layer nodes to obtain the network output resultD, as shown in Figure 4:Figure 4
Signal flow of BP algorithm.As shown in Figure4: in this stage, the input samples are passed from the input layer, each layer is processed through each hidden layer and then passed to the output layer. If the actual output of the output layer does not match the expected output, enter the second stage, the background attribute stage [21–24].The core idea of the algorithm is to divide the learning process into the following two stages.


### 2.2.1. Forward Propagation Stage

A forward propagation algorithm, as the name suggests, is an algorithm that proceeds from front to back. At the beginning of the introduction, initialize a set of values and select an activation function, compare the gap between the output result and the expected value. If it does not meet the requirements, the error is propagated back in the network. In the propagation, the error is allocated to each node of each layer so as to adjust the weight of each layer.The output of the first neuron in the hidden layer at this stage is as follows:(7)xf=f∑i=0nvijxi,j=1,2,…,m.The output of the nth neuron in the output layer is as follows:(8)Dn=f∑j=0mwjkxf,n=1,2,…,k.The error function is as follows:(9)E=12∑k=1tdn−on.


### 2.2.2. Back Propagation Stage

At this stage, the error is sent to the input layer through the hidden layer, and the network allocates errors to the units of each layer to obtain the error signal of the units of each layer. On this basis, the number of units is corrected. The mathematical expression of this stage is as follows:(10)Δyjk=−η∂E∂yjk=ηdn−onon1−onxi.The weight of the output layer is adjusted to the following formula:(11)ΔVjk=−η∂E∂Vjk=ηdn−onon1−xnyi.


## 2.2.1. Forward Propagation Stage

A forward propagation algorithm, as the name suggests, is an algorithm that proceeds from front to back. At the beginning of the introduction, initialize a set of values and select an activation function, compare the gap between the output result and the expected value. If it does not meet the requirements, the error is propagated back in the network. In the propagation, the error is allocated to each node of each layer so as to adjust the weight of each layer.The output of the first neuron in the hidden layer at this stage is as follows:(7)xf=f∑i=0nvijxi,j=1,2,…,m.The output of the nth neuron in the output layer is as follows:(8)Dn=f∑j=0mwjkxf,n=1,2,…,k.The error function is as follows:(9)E=12∑k=1tdn−on.


## 2.2.2. Back Propagation Stage

At this stage, the error is sent to the input layer through the hidden layer, and the network allocates errors to the units of each layer to obtain the error signal of the units of each layer. On this basis, the number of units is corrected. The mathematical expression of this stage is as follows:(10)Δyjk=−η∂E∂yjk=ηdn−onon1−onxi.The weight of the output layer is adjusted to the following formula:(11)ΔVjk=−η∂E∂Vjk=ηdn−onon1−xnyi.


## 2.3. Nguyen-Widrow Method

There are the randomization method and the Nguyen-Widrow method for initializing weights and thresholds. In order to solve the problem of slow convergence speed in the application of the neural network in the application of the dissolved gas analysis transformer fault diagnosis in the oil, the Nguyen-Wid-row method is used to initialize the variable parameters of the neural network. Normally, the calculation results show that the randomization method does not seem to be very suitable. In the face of more complex nonlinear systems, the randomization method will lead to low computational efficiency [25]. However, the initialization weight and threshold method proposed by Nguyen and Widrow can achieve the effect of significantly improving the calculation efficiency. In this way, the initial weight Q is as follows:(12)Q1=0.5×mn×normr2randm,n−Im,n.And the initialization thresholda is as follows:(13)a1=0.5×mn×2randm,n−Im,n.If the sample is not processed, it is directly used as the input sample data for BP neural network learning and training because the data unit is inconsistent, which will cause a greater impact on the range of the transfer function. It may cause the algorithm to oscillate and not converge, so it is necessary to standardize the samples, normr(Q) is the standardized normalization matrix of matrix Q: if Q=a1a2b1b2, then:(14)normrQ=a1a1+a1a2a2+a2b1b1+b1b2b2+b2.The initial weights and thresholds of neurons in each layer are random numbers between 0 and 1. The graph of tansig is shown in Figure5:Figure 5
Graph of tansig function.As shown in Figure5: BP network has excellent nonlinear function approximation ability. BP network with only one hidden layer can realize this function. The introduction of multiple hidden layers will speed up the training speed. Experiments show that for the problem to be solved in this article, even in the case of only one hidden layer, the training speed of the BP network can be satisfactory.The error between the output layer and the hidden layer is calculated as follows:(15)ej=qj−oj,where ej is the actual output of the jth neuron in the output layer and oj is the expected output. The error of calculating the hidden layer neuron is as follows:(16)ek=∑j=1iejwjk.The corrected value for calculating the weight of the output layer is as follows:(17)Δek=lrdjyk.In the formula,lr is the learning rate, dj is the local gradient, and yk is the hidden layer output. In the same way, the correction amount for calculating the hidden layer weight is as follows:(18)Δwki=lrdixk.The threshold correction value of the output layer and the hidden layer is as follows:(19)ΔaJ=lrej.If the error accuracy requirement is met, it is as follows:(20)Yi=Yi−YmaxYmax−Ymin.In the neural network, 10 sets of data are used as training samples, and the 11th sample data is used for prediction. Before the error reaches the target error requirement or reaches the set number of cycles, input training samples for learning and training in the BP neural network model. The error curve after learning is shown in Figure6:Figure 6
Error graph.As shown in Figure6: although the accuracy of the empirical prediction results is high, it should be noted that the premise of the prediction is to ensure that the original sample data is true, reasonable, and effective. Otherwise, no matter how high the accuracy is, the results will be meaningless. For determining whether the weight of the predictive index will affect the accuracy, it can be verified by the BP neural network prediction directly through the uncertainty of the index weight.In order to better record the credit credibility of participants in the sharing economy activities, the membership system of the sharing economy was born. Especially in the prevalence of the P2P business model, more and more transparent and true credit management system is gradually formed. The sharing economy business model has derived a variety of membership models, but the social credit mechanism of virtual social capital has actually produced constraints and incentives. Let the credit system of sharing economy participants bind users through members, generate stable user stickiness, gradually improve the credit investigation system, and reduce the credit cost of sharing economy transactions [26, 27]. The membership model of the sharing economy is shown in Figure 7.Figure 7
Membership model diagram.As shown in Figure7, different types of sharing economy business models rely on different relationship structures to produce corresponding changes. This relationship is not static, and it changes differently under certain conditions and circumstances. This change, in turn, will also affect the resources and models of the sharing economy platform.


## 3. Experiment

### 3.1. Experiments of the Questionnaire Survey

The sharing economy has become a visible trend of modern business models, and it has been implemented in various industries such as catering, housing, transportation, logistics, and tourism. In the future, this article believes that the development direction of the sharing economy mainly has the following aspects: there will be extensive exposure to the sharing economy and a more affordable and convenient sharing economy. Innovation and customer value propositions may become more direct. Secondly, the sharing economy will develop to the entire industry; that is, the sharing economy will gradually expand to various industries and fields. Thirdly, education and medical fields will be the key areas for the development of the sharing economy because they have great potential in key resources and profit model innovation. Finally, the sharing economy will develop into the whole process. The sharing economy will play a role in both production and consumption, will also play a role in the field of distribution and circulation, and will directly affect the key processes of the business model [28, 29].With the improvement of people’s economic level, the increasing demand for people to travel, and the popularity of Internet technology and foreign sharing economy models in China, more and more people use shared bicycles to travel. Through the sharing platform built on the Internet, people can use shared bicycles or cars only by scanning the code on their mobile phones. While solving the problem of people’s travel, it greatly reduces people’s travel costs and can also make resources recycling. This article surveys the travel of 10 office workers, as shown in Table1:Table 1
Survey of the travel situation of 10 office workers.
Survey objectWhether to buy a carUse shared bikesUse the purchased car1YesYesNo2YesYesNo3YesYesNo4YesYesNo5NoNoNo6YesYesNo7YesYesNo8YesYesYes9NoYesYes10YesYesNo11YesYesYes12YesYesYesAs shown in Table1: the 12 office workers surveyed all have their own cars, but only 2 of them are willing to use their own cars when the traffic is not congested. There are 10 people who are willing to use shared bicycles. It can be seen from this that the utilization rate of shared bicycles has reached 80%. They believe that using shared bicycles not only saves time but also saves parking fees and gas costs, which makes them more willing to choose shared bicycles.In this paper, a total of 120 people of different ages are surveyed on the factors of choosing shared cars, as shown in Table2:Table 2
120 questionnaire on selection factors of different ages.
Survey objectMore comfortableSave moneySave timeMore convenient25–351013121535–451098845–551076555–652323As shown in Table2, among 120 people of different ages, the factors for choosing a shared car can be classified into four categories: more convenient, more comfortable, more cost-effective, and more time-saving. Among them, there are a large number of people aged 25–35 who choose to share cars, and 15 people choose the factor of more convenience. They believe that the biggest factor in choosing a shared car is more convenient. People aged 55–65 rarely use shared cars because they do not know how to operate mobile phones. It can be seen that the majority of people still use shared cars, reflecting from the side that the development of the sharing economy has been welcomed by people.This article then conducted a survey on 100 people of different identities (students, office workers, and nonemployed people). Also, it made a statistics on the percentage of factors selected by different ages, as shown in Figure8:Figure 8
100 statistics of different identities.As shown in Figure8: among the 100 respondents, the probability of choosing shared bicycles for 15–25 years old is about 35%; the probability that 25–35 years old is willing to use shared bicycles is about 38%; the probability of 35–45 years old willing to use shared bicycles is also around 40%. It can be seen from this that students choose shared cars the most, unemployed people choose public transportation because they do not go out much, or because they are too old, they choose to walk.This article investigates the development trend of the sharing economy in the two time periods of 2017–2018 and 2019–2020, as shown in Figure9:Figure 9
The development trend of the sharing economy in the two time periods of 2017–2018 and 2019–2020.As shown in Figure9, from 2017 to 2018, the overall development trend of the sharing economy is very unstable, with highs and lows, with the lowest 8% and the highest 16%. From 2019 to 2020, the overall development trend of the sharing economy is on the rise, except for the lowest 10% in May-June, and the highest is 25% in May-June 2020. In general, the development of the sharing economy is still very rapid.


### 3.2. Neural Network Experiment

The sharing economy will develop in accordance with the five main lines of enterprises, governments, cities, individuals, and society as a whole. Therefore, strengthening policies to protect the interests of the participants in the sharing economy is conducive to the sustainable development of the sharing economy. For example, a two-way scoring mechanism can be used for the leasing model, and economic leverage rewards and punishments can be implemented. For the platform model, it is necessary to pay attention to the compliance of the product or service provided and the guarantee of the platform’s risk control system. Since the sharing economy is built on the Internet platform, the Internet economy has intensified industrial integration and blurred the boundaries between traditional industries. In the Internet age, products, services, and even industries need to be redefined. The sharing economy has also brought about tremendous changes in the participants of commercial activities and the boundaries of enterprises.In the experiment, a three-layer network model is designed, in which the input layer nodes are: predicted value, true value, error, and error rate. According to the formula, the number of hidden layer nodes is initially determined, and the number of output layer nodes is 4. Therefore, the network structure is initially determined, as shown in Table3:Table 3
Forecast error table from 2011 to 2015.
yearsPredictive valueActual valueErrorError rate (%)2011210.6210.40.20.122012223.5223.10.40.102013245.4245.30.10.082014267.6267.80.20.062015289.7289.50.20.05It can be seen from Table3 that the error rate was 0.12% in 2011, 0.10% in 2012, and only 0.05% in 2015. It can be seen that the error is getting smaller and smaller, the fitting effect of the network structure is good, and the error between the real value and the predicted value is almost nonexistent. This shows that the training effect of the model has reached the standard, has a strong degree of fit, and can be used to make economic forecasts in future years.The network obtained through the above repeated attempts from 2011 to 2015 has strong generalization ability, and the prediction error is within the allowable range, which can be used for economic prediction. The trained network can predict 2016–2020 in the trained network, as shown in Table4.Table 4
2016–2020 forecast error table.
yearsPredictive valueActual valueErrorError rate (%)2016287.56287.630.060.082017298.53298.580.050.072018302.31302.270.040.062019327.87327.850.020.042020347.78347.770.010.02It can be seen from Table4 that the error rate was 0.08% in 2016 and 0.07% in 2018. By 2020, the error was only 0.02%. It can be seen that the error is getting smaller and smaller, compared with 2011–2015, the error rate is 27% smaller. It can also be seen from this that the effect of using neural network structure to model the sharing economy and new business capabilities is very good, so it is completely possible to use a neural network to simulate.This paper compares the error rates of 170 sets of data with traditional models and neural network algorithm models. The output error curve is shown in Figure10.Figure 10
Comparison of error rate between the traditional model and neural network algorithm model.As shown in Figure10: in the traditional model, it can be seen that the input data of the 155th group deviates far from other data, and the expected output value is far from other data, so it is the maximum possible output value of the traditional algorithm model. So the biggest error appears at the output of the 155th network. Even so, the training error here is at most 0.35%, and the overall error rate is between 0.25% and 0.35%. From the BP neural network algorithm model, it can be seen that when the data with the largest error is in the 65 groups, the error rate is 0.33%, and the overall error rate is between 01% and 0.3%. The error rate is much lower than that of traditional algorithms, and the training accuracy is very good. The predictive ability of neural networks is closely related to the quantity and quality of historical data for training. Although the neural network has certain fault tolerance, factors such as the objectivity of the score and the degree of dispersion of training samples in the specific application process will affect the prediction accuracy of the neural network. Therefore, in the application process, when conditions permit, the quality and quantity of training samples should be improved to ensure the performance of the neural network.


## 3.1. Experiments of the Questionnaire Survey

The sharing economy has become a visible trend of modern business models, and it has been implemented in various industries such as catering, housing, transportation, logistics, and tourism. In the future, this article believes that the development direction of the sharing economy mainly has the following aspects: there will be extensive exposure to the sharing economy and a more affordable and convenient sharing economy. Innovation and customer value propositions may become more direct. Secondly, the sharing economy will develop to the entire industry; that is, the sharing economy will gradually expand to various industries and fields. Thirdly, education and medical fields will be the key areas for the development of the sharing economy because they have great potential in key resources and profit model innovation. Finally, the sharing economy will develop into the whole process. The sharing economy will play a role in both production and consumption, will also play a role in the field of distribution and circulation, and will directly affect the key processes of the business model [28, 29].With the improvement of people’s economic level, the increasing demand for people to travel, and the popularity of Internet technology and foreign sharing economy models in China, more and more people use shared bicycles to travel. Through the sharing platform built on the Internet, people can use shared bicycles or cars only by scanning the code on their mobile phones. While solving the problem of people’s travel, it greatly reduces people’s travel costs and can also make resources recycling. This article surveys the travel of 10 office workers, as shown in Table1:Table 1
Survey of the travel situation of 10 office workers.
Survey objectWhether to buy a carUse shared bikesUse the purchased car1YesYesNo2YesYesNo3YesYesNo4YesYesNo5NoNoNo6YesYesNo7YesYesNo8YesYesYes9NoYesYes10YesYesNo11YesYesYes12YesYesYesAs shown in Table1: the 12 office workers surveyed all have their own cars, but only 2 of them are willing to use their own cars when the traffic is not congested. There are 10 people who are willing to use shared bicycles. It can be seen from this that the utilization rate of shared bicycles has reached 80%. They believe that using shared bicycles not only saves time but also saves parking fees and gas costs, which makes them more willing to choose shared bicycles.In this paper, a total of 120 people of different ages are surveyed on the factors of choosing shared cars, as shown in Table2:Table 2
120 questionnaire on selection factors of different ages.
Survey objectMore comfortableSave moneySave timeMore convenient25–351013121535–451098845–551076555–652323As shown in Table2, among 120 people of different ages, the factors for choosing a shared car can be classified into four categories: more convenient, more comfortable, more cost-effective, and more time-saving. Among them, there are a large number of people aged 25–35 who choose to share cars, and 15 people choose the factor of more convenience. They believe that the biggest factor in choosing a shared car is more convenient. People aged 55–65 rarely use shared cars because they do not know how to operate mobile phones. It can be seen that the majority of people still use shared cars, reflecting from the side that the development of the sharing economy has been welcomed by people.This article then conducted a survey on 100 people of different identities (students, office workers, and nonemployed people). Also, it made a statistics on the percentage of factors selected by different ages, as shown in Figure8:Figure 8
100 statistics of different identities.As shown in Figure8: among the 100 respondents, the probability of choosing shared bicycles for 15–25 years old is about 35%; the probability that 25–35 years old is willing to use shared bicycles is about 38%; the probability of 35–45 years old willing to use shared bicycles is also around 40%. It can be seen from this that students choose shared cars the most, unemployed people choose public transportation because they do not go out much, or because they are too old, they choose to walk.This article investigates the development trend of the sharing economy in the two time periods of 2017–2018 and 2019–2020, as shown in Figure9:Figure 9
The development trend of the sharing economy in the two time periods of 2017–2018 and 2019–2020.As shown in Figure9, from 2017 to 2018, the overall development trend of the sharing economy is very unstable, with highs and lows, with the lowest 8% and the highest 16%. From 2019 to 2020, the overall development trend of the sharing economy is on the rise, except for the lowest 10% in May-June, and the highest is 25% in May-June 2020. In general, the development of the sharing economy is still very rapid.


## 3.2. Neural Network Experiment

The sharing economy will develop in accordance with the five main lines of enterprises, governments, cities, individuals, and society as a whole. Therefore, strengthening policies to protect the interests of the participants in the sharing economy is conducive to the sustainable development of the sharing economy. For example, a two-way scoring mechanism can be used for the leasing model, and economic leverage rewards and punishments can be implemented. For the platform model, it is necessary to pay attention to the compliance of the product or service provided and the guarantee of the platform’s risk control system. Since the sharing economy is built on the Internet platform, the Internet economy has intensified industrial integration and blurred the boundaries between traditional industries. In the Internet age, products, services, and even industries need to be redefined. The sharing economy has also brought about tremendous changes in the participants of commercial activities and the boundaries of enterprises.In the experiment, a three-layer network model is designed, in which the input layer nodes are: predicted value, true value, error, and error rate. According to the formula, the number of hidden layer nodes is initially determined, and the number of output layer nodes is 4. Therefore, the network structure is initially determined, as shown in Table3:Table 3
Forecast error table from 2011 to 2015.
yearsPredictive valueActual valueErrorError rate (%)2011210.6210.40.20.122012223.5223.10.40.102013245.4245.30.10.082014267.6267.80.20.062015289.7289.50.20.05It can be seen from Table3 that the error rate was 0.12% in 2011, 0.10% in 2012, and only 0.05% in 2015. It can be seen that the error is getting smaller and smaller, the fitting effect of the network structure is good, and the error between the real value and the predicted value is almost nonexistent. This shows that the training effect of the model has reached the standard, has a strong degree of fit, and can be used to make economic forecasts in future years.The network obtained through the above repeated attempts from 2011 to 2015 has strong generalization ability, and the prediction error is within the allowable range, which can be used for economic prediction. The trained network can predict 2016–2020 in the trained network, as shown in Table4.Table 4
2016–2020 forecast error table.
yearsPredictive valueActual valueErrorError rate (%)2016287.56287.630.060.082017298.53298.580.050.072018302.31302.270.040.062019327.87327.850.020.042020347.78347.770.010.02It can be seen from Table4 that the error rate was 0.08% in 2016 and 0.07% in 2018. By 2020, the error was only 0.02%. It can be seen that the error is getting smaller and smaller, compared with 2011–2015, the error rate is 27% smaller. It can also be seen from this that the effect of using neural network structure to model the sharing economy and new business capabilities is very good, so it is completely possible to use a neural network to simulate.This paper compares the error rates of 170 sets of data with traditional models and neural network algorithm models. The output error curve is shown in Figure10.Figure 10
Comparison of error rate between the traditional model and neural network algorithm model.As shown in Figure10: in the traditional model, it can be seen that the input data of the 155th group deviates far from other data, and the expected output value is far from other data, so it is the maximum possible output value of the traditional algorithm model. So the biggest error appears at the output of the 155th network. Even so, the training error here is at most 0.35%, and the overall error rate is between 0.25% and 0.35%. From the BP neural network algorithm model, it can be seen that when the data with the largest error is in the 65 groups, the error rate is 0.33%, and the overall error rate is between 01% and 0.3%. The error rate is much lower than that of traditional algorithms, and the training accuracy is very good. The predictive ability of neural networks is closely related to the quantity and quality of historical data for training. Although the neural network has certain fault tolerance, factors such as the objectivity of the score and the degree of dispersion of training samples in the specific application process will affect the prediction accuracy of the neural network. Therefore, in the application process, when conditions permit, the quality and quantity of training samples should be improved to ensure the performance of the neural network.


## 4. Discussion

This paper analyzes the development based on the BP neural network system and new business models, expounds on the related concepts of the sharing economy and new business models, studies the relevant theories based on the BP neural network system, and explores the methods for the development of the sharing economy and new business models, and through the questionnaire survey method case to discuss the importance of the sharing economy to contemporary society, and finally take the sharing economy into the BP neural network as an example to explore the relationship between the two.This article also makes reasonable use of the BP neural network algorithm. As the range of applications of BP neural network algorithms has become more extensive and its importance has gradually become more prominent, many scholars have begun to match the theory of BP neural network algorithms with real-life application scenarios and propose feasible algorithms. The BP neural network algorithm is a mathematical operation. According to this operation, it is an indispensable part of life to strengthen the new business model through the sharing economy.Through the questionnaire survey method, it can be known that the sharing economy business model has driven the reuse of the entire social resources, but any emerging business model will face many opportunities and challenges in the initial stage of development and the development process, and there will also be some problems. It needs to be optimized through continuous theoretical exploration and practical improvement.


## 5. Conclusions

This article mainly starts from theoretical knowledge based on BP neural network and sharing economy and discusses how to establish a set of new business models with higher reliability based on BP neural network. Based on the BP neural network algorithm and neural network structure, it can be seen that in the process of applying the BP neural network algorithm to the sharing economy, it can obtain better evaluation results and provide a new idea for new business models, and the process is faster and more accurate, which has certain guiding significance for how to develop new business models. Based on the wide range of related scientific fields involved in the BP neural network research, the concept of the sharing economy has always been disputed. The author’s knowledge has not yet reached the perfect position, and the academic theory and professional ability are relatively weak. Due to the author’s limited cognitive level and energy, the process analysis and conclusion summary may be affected. There are still many shortcomings in the article, but the author will try to do better.



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*Source: 4974564-2022-04-07.xml*