# Digital Protection and Utilization of Folk Art Resources Using Virtual Reality Technology

Authors: Shuo Wang
Journal: Mobile Information Systems
 (2022)

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## Abstract

Folk art is a “living fossil” of hisory, whose existence and evolution reflect the way people live and think. It is a one-of-a-kind way of communicating the process of human development to the rest of the world. However, as China’s economic development and cultural globalization accelerate, many folk arts are being severely harmed and lost. The emergence of virtual reality technology, which benefits from the advantages of virtual reality technology, has greatly strengthened the utilization and protection of folk art resources in recent years, with the rapid development of Internet technology. Virtual reality technology is a human-computer interaction experience technology that uses complex calculations to provide corresponding operational feedback to the senses of touch, hearing, vision, and balance. It is more reasonable and perfect to reconstruct and integrate the resource content and realize the digital utilization of folk art resources in this paper, which is based on the ALS collaborative filtering algorithm. This paper will make some necessary improvements to the original algorithm, which has met the requirements of virtual reality technology, through the comparison of algorithms. Finally, experiments show that the proposed algorithm’s accuracy is 85 percent higher than the original algorithms, and the data retrieval processing interval is more reasonable.


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## Body

## 1. Introduction

My country is multi-ethnic, and over the course of its long history of development, the Chinese people have amassed a rich folk art cultural heritage with significant folk art heritage, social value, and economic value [1]. People’s urbanization has been steadily increasing in tandem with China’s ongoing and rapid urbanization and economic modernization [2]. In this objective and realistic environment, the original ecological cultural space on which survival depends has been severely damaged and influenced, putting many folk art resources in grave danger of extinction, and some of them are even on the verge of extinction [3]. Virtual reality technology is an emerging art form technology that is in line with the development of the times but differs from traditional art in the folk art category. Under the influence of virtual reality technology, the original single painting art has appeared a lot (VR). Various forms range from static performance to dynamic art display, from passive aesthetic appreciation to outright acceptance of artistic beauty [4]. Virtual and interactive art has emerged against the backdrop of virtual reality technology.The background lies in the rapid development of computers, and the subsequent impact of virtual technology gives room for improvement in various fields, according to interactive research of virtual technology and art in today’s society under the virtual reality technology environment. As a result, the impact of artistic pictures and the convenience provided by functions are accompanied by the combination of virtual reality technology and art [5]. Virtual reality technology art is defined by the characteristics of digital information generated in a virtual reality technology environment. The integration of virtual technology and art, with the accumulation of many forms of artistic expression, requires artists to make a new statement and create a new form that can use numerical demonstration and programming code [6]. Visitors will be able to better understand folk art if virtual reality technology is integrated into the physical exhibition [7].Taking digital protection and utilization as the basic principle and the fundamental starting point, the overall goal of this research is aiming at the current problems of digital development, utilization and protection in my country, taking the latest cutting-edge technology augmented reality as the core foundation, integrating the use of computer graphics image, digital animation, cloud storage services, and other modern scientific and technological means, exploring the basic ideas and practical methods of seeking technology-based digital development and utilization, and forming a systematic technology-based digital development and utilization, digital display, digital dissemination, and other digital development and utilization [8, 9]. The following are the innovations of this paper based on virtual reality technology and digitalization of folk art resources:(1)
With the advantages of rich and colorful folk art resources in our country, we can use virtual reality technology for better presentation, which will greatly protect those art resources that are not easy to repair and protect [10]. On this basis, the collaborative filtering algorithm based on ALS reconstructs and integrates the resource content, realizes the innovation of folk art, makes it get better vitality with the support of virtual technology, and realizes the complementarity between technology and art.(2)
The technical effect of virtual reality technology is often single, but the folk art resources are extremely complicated [11]. Therefore, we need to carry out diversified design; that is, we can fully reflect and express folk art through diversified design, so that the experiencer can be immersed in the contrast of cultural emotions, and change from the original fixed perspective to a free perspective.The introduction is the first section, and it describes the background and significance of the research as well as the article’s innovation. The second section summarizes relevant research findings from both domestic and international literature, as well as the paper’s research ideas. The method section is found in the third section. This paper focuses on the use of virtual reality technology and a collaborative filtering algorithm to protect and utilize folk art resources. Experiment and data processing are covered in the fourth section. It is proposed that the improved algorithm in this paper improves the reliability and rationality of the protection and utilization of folk art resources by 85 percent when compared to the original algorithm. The fifth section is a summary of the design proposed in this paper on the original system.


## 2. Related Work

The results of a survey report by Hong show that augmented reality and its application research is a development trend of great strategic significance in the next year, which will affect the characteristics of social development in a certain period of time around the world [12]. Ebner et al. gave the definition of augmented reality: augmented reality is a real-time direct or indirect view of the real physical world environment, in which some sensory information based on computer generation, such as sound, video, images, or data, is input into the real physical world environment, thus increasing or enhancing the components of the real environment [13]. In virtual reality by Thomas et al., we learned about the development process of virtual reality and how it is combined with business. Through a large number of cases, we analyzed the prospects from now to the future and the current bottlenecks [14]. In the research on the interaction and integration of technology and art proposed by Wang, many researchers still focus on the development of technology, but do not pay attention to the decisive factor that art plays in works [15]. Zhou Zhi’s research shows that the interactive content is suitable for not only various versions of web browsing, but also browsing methods such as video graphics and virtual panoramas. Each column has been carefully designed [16]. The digital reconstruction of monasteries proposed by Zhou and the virtual St. Sofia Cathedral of Geneva University in Switzerland are very significant digital virtual projects [17]. In the book VR Revolution: How Virtual Reality will Change Our Life, Li not only talks about the definition of virtual reality technology, but also mentions that the popularity of virtual reality technology in today’s society covers almost all fields [18]. The second category summarizes and analyzes the functions of the physical art museum. Zeng and Dong mentioned in the research on exhibition and exhibition of China Art Museum that “as a public welfare cultural institution, the main function of the art museum is exhibition, including the educational function in the art museum, and the usual form is interactive activities through exhibition and display” [19]. In the digital construction and development of art galleries, Jiang proposed that “digital art galleries are produced by physical art galleries in order to adapt to the development and challenges of the information age, which is the inevitable trend of the development of art galleries in today’s era” [20]. The Revit software proposed by Kang and Kim is extremely fast in plotting, with its own data statistics and powerful data management and processing capabilities [21]. Wang and Zhao put forward that the emergence of virtual reality technology conforms to the development of the times and the future development trend. Inheriting and carrying forward the factors and aesthetic connotation of folk art design can provide inspiration for the concept of modern cultural design and make the domestic virtual reality technology design truly represent and show the national spirit and cultural connotation [22].Based on the research of the abovementioned related work, this paper determines the positive role of virtual reality technology in the field of digital application of folk art resources, constructs a new art resource integration technology based on virtual reality technology, and conducts in-depth analysis and discussion. Research and use virtual reality technology to acquire and collect folk art cultural resources utilize resources more effectively, tap the valuable value behind the digitization of resources, and discover potential problems affecting the inheritance and dissemination of folk art.


## 3. Methodology

### 3.1. An Overview of Virtual Reality Technology

As a new digital technology that began to rise in the twentieth century, it is the result of the long-term development of computer display technology, computer graphics technology, sensor technology, artificial intelligence [23, 24], and other fields [25]. Positive and unrestricted interaction can provide users with a similar immersive experience in the virtual world as they can in the real world. As the traditional passive and single interaction mode between human and computer is replaced, the interaction between the user and the system becomes active, diverse, and natural. Virtual reality technology is a type of human-computer interaction that uses complex calculations to generate corresponding operation feedback to the senses of touch, hearing, vision, and balance. Virtual reality technology also includes virtual data processing technologies like animation capture, image synthesis, sound filtering, and text editing as a complex systematic technology. Several core technologies that play a key role in virtual reality technology are listed below.


#### 3.1.1. Augmented Reality Technology

Augmented reality technology cannot directly process images and videos, and requires several processes to have a better application effect on the data. Figure1 shows a typical flowchart of augmented reality.Figure 1
Typical flowchart of augmented reality.Firstly, the augmented reality system obtains the external parameter data of the camera by performing a conversion calculation between the world coordinate system, camera coordinate system, imaging plane coordinate system, and image coordinate system, which determines the relative position and attitude information of the camera. The virtual digital content information is thus rendered and generated in real time, superimposed in the real scene via virtual real fusion, and displayed and output via the display device.


#### 3.1.2. Three-Dimensional Tracking Technology

The camera’s position and posture are mostly determined by recognizing the features of the images in the real scene. Methods based on artificial markers and methods based on natural image features are the two types of registration methods. By identifying parallel lines, vertical lines, plane objects, corners, and texture features of markers in the real scene in real time, the system obtains the camera’s internal and external parameters. Determine the position and direction of the camera in three-dimensional space in relation to the real environment based on the acquired characteristics of the real environment video image frame, in order to accurately place the virtual object in the real scene. 3D tracking and registration technology is the system’s core key technology for achieving seamless integration of virtual objects and real scenes. Among them, the image design is extremely important. The following formula is used to match the obtained image to the best image area because the captured image features must be modified and processed. In the rectangular block model of art design, the Jacobian iterative algorithm is used to repair the brightness balance of the image. The coefficient matrixAn×n of the Jacobian transform of the image is input, the adjacent element vector is bn×1, the edge fusion error is ε, and the initial solution vector is xn×1. The edge pixel ϕ=θ of the repair and positioning center of the environmental art design image area is taken. Let λ=δ/β, F=U2+V2 to obtain the conductivity equation of the best matching block area as follows:(1)hx,y=λ2πσ2exp−12x′2σ2+λ2y′2σ2⋅exp2πjFx′.Select the minimum error rate, calculate the inter-class bit rate, take the center frequency as the rotation invariant moment, and get the following formula:(2)φn=πkN,k=0,1,2,…,N−1.The best matching block window function of the image distribution is obtained by using the pixel confidence optimization method, and the adjustment formula of the priority coefficient to be repaired quickly is(3)x′y′1=cos−θ1−sin−θ10sin−θ1cos−θ10001xy1.The original environmental art design image’s two-dimensional distribution coordinates are shown above. Each sub-block at the damaged edge performs vector quantization decomposition of adjacent edges, and the image brightness equalization repair is realized in conjunction with update iteration.


### 3.2. The Expression of Virtual Reality Technology in Folk Art Resources

As we all know, science and technology, as the cornerstone of human progress, leads the renewal of all aspects of society. In the art field, the artist’s art creation field provides foresight guidance in the innovation of science and technology. In the fields of technology and art, their influence on each other is mutual. Figure2 shows the design process of folk art resources under virtual reality technology.Figure 2
Flowchart of virtualization design of folk art resources.The advancement of digital technology has an impact on the content of our lives and enriches the way we interact with the world, and the advent of virtual reality technology has broadened our perspectives on the world. People’s traditional physical artworks, such as canvases, paper, and walls in ancient times, have undergone a revolutionary transformation as a result of the integration of virtual reality technology. These artworks, which are easily worn out over time, have undergone a revolutionary transformation. More and more artists in society are interested in seeing if this new technology can bring about changes and breakthroughs in art, as well as what kind of new experience it can provide.Because the collaborative filtering algorithm is required for the virtualization of folk art resources, this paper concentrates on the content of the algorithm. To begin, the core principle of the ALS-based matrix decomposition algorithm is to use an iterative method to solve a series of least square regression problems using a fixed user or item factor matrix. Generally, the similar matrixX of the product of two low rank matrices can be found to replace the matrix X evaluated by the user. The principle formula is as follows:(4)R≈X=UMT.Among them,U∈Cm×d,M∈Cn×d,d represents the number of implicit factors that lead to the user’s evaluation of the item. Since the user evaluation matrix R is approximately equal to X, the loss function can be expressed as(5)LU,M=∑i,j∈TRij−UiMjT2+λUi2+Mi2.The regularization term of the above formula is(6)LU,M=∑i,j∈TRij−UiMjT2+λUi2+Mi2.Every time the characteristics of each user are updated iteratively, fixedly, and one by one, the partial derivative can be solved:(7)Ui=RiMuiTMuiTM+λnuiI−1,i∈1,M.Find the matrix and the way to find the matrix, and then we can get(8)Mj=RjTUmjUmjmTUmj+λmjI−1,j∈1,n.Through the continuous iteration of the above formula, the similarity matrixU of the user evaluation matrix M can be calculated by taking advantage of the matrix R and the transformation matrix of the matrix X.


### 3.3. Digital Value Analysis of Folk Art Resources

The public is gradually becoming aware of the allure of traditional art digitalization. This method of presenting the world with the unique beauty of folk art is based on a digital museum design created by combining art and digital display technology. In the case of traditional folk art, digital display brings the artistic conception closer to life and more “earthy,” gradually shifting the standard of value evaluation of traditional art in previous humanistic aesthetic activities. Simultaneously, it demonstrates the reality of the conjecture of virtual display of traditional art in the real world, as well as providing some reference and inspiration value for people working on digital displays of other traditional aesthetics. In terms of the humanities, folk art is full of life flavor and authentic public aesthetics because it comes directly from the masses of people. From a communication standpoint, the dissemination of folk art resources through digitalization must first be presented to people as works of art. It is also the public’s recognition of these folk arts that is based on browsing the digital dissemination of folk art resources. Furthermore, this can allow people to directly experience the value enhancement of traditional culture as science and technology advances. As one of the art display media with vitality and development potential, digital media is a media culture with social service attribute developed based on human imagination and creativity. Figure3 shows the flowchart of digital processing of folk art.Figure 3
Flowchart of digital processing of folk art.Relying on this, the digital display of folk art emerges as the times require. With its unique form of expression, folk art can win more public support from users for the digital display form on the basis of its own inherent cultural value. Due to the easy dissemination and replicability of cultural digital exhibits, digital technology provides convenient conditions for the creation and transmission of mass art. At the same time, it encourages artworks to be presented in a digital way. To a certain extent, it increases people’s impulse to contact and collect the physical works of art being disseminated in digital media. But there is a need for similarity that is different from people’s traditional cultural aesthetics, and different folk arts are not the same. For the abovementioned similarity problem, this paper proposes to use the calculation of project similarity. As a result, we use Pearson correlation coefficient to calculate the similarity; then,(9)simi,j=∑c∈IijRi,c−R¯iRj,c−R¯j∑c∈IijRi,c−R¯i2∑c∈IijRj,c−R¯j2.The loss function after incorporating the similarity calculation is as follows:(10)fU,M=∑i,j∈TRij−UiMjT2+λUi2+Mi2+∑c∈IijRj,c−R¯iRj,c−R¯j∑c∈IijRi,c−R¯i2∑c∈IijRj,c−R¯j2.Among them, the common evaluation set of useri,j and their two users on the project is Iij,Ri,c, which is the evaluation of user i on the project c, Rj,c is the evaluation of user j on the project c, and R¯i,R¯j is the average evaluation of user i,j on the project XXX, respectively.


## 3.1. An Overview of Virtual Reality Technology

As a new digital technology that began to rise in the twentieth century, it is the result of the long-term development of computer display technology, computer graphics technology, sensor technology, artificial intelligence [23, 24], and other fields [25]. Positive and unrestricted interaction can provide users with a similar immersive experience in the virtual world as they can in the real world. As the traditional passive and single interaction mode between human and computer is replaced, the interaction between the user and the system becomes active, diverse, and natural. Virtual reality technology is a type of human-computer interaction that uses complex calculations to generate corresponding operation feedback to the senses of touch, hearing, vision, and balance. Virtual reality technology also includes virtual data processing technologies like animation capture, image synthesis, sound filtering, and text editing as a complex systematic technology. Several core technologies that play a key role in virtual reality technology are listed below.


### 3.1.1. Augmented Reality Technology

Augmented reality technology cannot directly process images and videos, and requires several processes to have a better application effect on the data. Figure1 shows a typical flowchart of augmented reality.Figure 1
Typical flowchart of augmented reality.Firstly, the augmented reality system obtains the external parameter data of the camera by performing a conversion calculation between the world coordinate system, camera coordinate system, imaging plane coordinate system, and image coordinate system, which determines the relative position and attitude information of the camera. The virtual digital content information is thus rendered and generated in real time, superimposed in the real scene via virtual real fusion, and displayed and output via the display device.


### 3.1.2. Three-Dimensional Tracking Technology

The camera’s position and posture are mostly determined by recognizing the features of the images in the real scene. Methods based on artificial markers and methods based on natural image features are the two types of registration methods. By identifying parallel lines, vertical lines, plane objects, corners, and texture features of markers in the real scene in real time, the system obtains the camera’s internal and external parameters. Determine the position and direction of the camera in three-dimensional space in relation to the real environment based on the acquired characteristics of the real environment video image frame, in order to accurately place the virtual object in the real scene. 3D tracking and registration technology is the system’s core key technology for achieving seamless integration of virtual objects and real scenes. Among them, the image design is extremely important. The following formula is used to match the obtained image to the best image area because the captured image features must be modified and processed. In the rectangular block model of art design, the Jacobian iterative algorithm is used to repair the brightness balance of the image. The coefficient matrixAn×n of the Jacobian transform of the image is input, the adjacent element vector is bn×1, the edge fusion error is ε, and the initial solution vector is xn×1. The edge pixel ϕ=θ of the repair and positioning center of the environmental art design image area is taken. Let λ=δ/β, F=U2+V2 to obtain the conductivity equation of the best matching block area as follows:(1)hx,y=λ2πσ2exp−12x′2σ2+λ2y′2σ2⋅exp2πjFx′.Select the minimum error rate, calculate the inter-class bit rate, take the center frequency as the rotation invariant moment, and get the following formula:(2)φn=πkN,k=0,1,2,…,N−1.The best matching block window function of the image distribution is obtained by using the pixel confidence optimization method, and the adjustment formula of the priority coefficient to be repaired quickly is(3)x′y′1=cos−θ1−sin−θ10sin−θ1cos−θ10001xy1.The original environmental art design image’s two-dimensional distribution coordinates are shown above. Each sub-block at the damaged edge performs vector quantization decomposition of adjacent edges, and the image brightness equalization repair is realized in conjunction with update iteration.


## 3.1.1. Augmented Reality Technology

Augmented reality technology cannot directly process images and videos, and requires several processes to have a better application effect on the data. Figure1 shows a typical flowchart of augmented reality.Figure 1
Typical flowchart of augmented reality.Firstly, the augmented reality system obtains the external parameter data of the camera by performing a conversion calculation between the world coordinate system, camera coordinate system, imaging plane coordinate system, and image coordinate system, which determines the relative position and attitude information of the camera. The virtual digital content information is thus rendered and generated in real time, superimposed in the real scene via virtual real fusion, and displayed and output via the display device.


## 3.1.2. Three-Dimensional Tracking Technology

The camera’s position and posture are mostly determined by recognizing the features of the images in the real scene. Methods based on artificial markers and methods based on natural image features are the two types of registration methods. By identifying parallel lines, vertical lines, plane objects, corners, and texture features of markers in the real scene in real time, the system obtains the camera’s internal and external parameters. Determine the position and direction of the camera in three-dimensional space in relation to the real environment based on the acquired characteristics of the real environment video image frame, in order to accurately place the virtual object in the real scene. 3D tracking and registration technology is the system’s core key technology for achieving seamless integration of virtual objects and real scenes. Among them, the image design is extremely important. The following formula is used to match the obtained image to the best image area because the captured image features must be modified and processed. In the rectangular block model of art design, the Jacobian iterative algorithm is used to repair the brightness balance of the image. The coefficient matrixAn×n of the Jacobian transform of the image is input, the adjacent element vector is bn×1, the edge fusion error is ε, and the initial solution vector is xn×1. The edge pixel ϕ=θ of the repair and positioning center of the environmental art design image area is taken. Let λ=δ/β, F=U2+V2 to obtain the conductivity equation of the best matching block area as follows:(1)hx,y=λ2πσ2exp−12x′2σ2+λ2y′2σ2⋅exp2πjFx′.Select the minimum error rate, calculate the inter-class bit rate, take the center frequency as the rotation invariant moment, and get the following formula:(2)φn=πkN,k=0,1,2,…,N−1.The best matching block window function of the image distribution is obtained by using the pixel confidence optimization method, and the adjustment formula of the priority coefficient to be repaired quickly is(3)x′y′1=cos−θ1−sin−θ10sin−θ1cos−θ10001xy1.The original environmental art design image’s two-dimensional distribution coordinates are shown above. Each sub-block at the damaged edge performs vector quantization decomposition of adjacent edges, and the image brightness equalization repair is realized in conjunction with update iteration.


## 3.2. The Expression of Virtual Reality Technology in Folk Art Resources

As we all know, science and technology, as the cornerstone of human progress, leads the renewal of all aspects of society. In the art field, the artist’s art creation field provides foresight guidance in the innovation of science and technology. In the fields of technology and art, their influence on each other is mutual. Figure2 shows the design process of folk art resources under virtual reality technology.Figure 2
Flowchart of virtualization design of folk art resources.The advancement of digital technology has an impact on the content of our lives and enriches the way we interact with the world, and the advent of virtual reality technology has broadened our perspectives on the world. People’s traditional physical artworks, such as canvases, paper, and walls in ancient times, have undergone a revolutionary transformation as a result of the integration of virtual reality technology. These artworks, which are easily worn out over time, have undergone a revolutionary transformation. More and more artists in society are interested in seeing if this new technology can bring about changes and breakthroughs in art, as well as what kind of new experience it can provide.Because the collaborative filtering algorithm is required for the virtualization of folk art resources, this paper concentrates on the content of the algorithm. To begin, the core principle of the ALS-based matrix decomposition algorithm is to use an iterative method to solve a series of least square regression problems using a fixed user or item factor matrix. Generally, the similar matrixX of the product of two low rank matrices can be found to replace the matrix X evaluated by the user. The principle formula is as follows:(4)R≈X=UMT.Among them,U∈Cm×d,M∈Cn×d,d represents the number of implicit factors that lead to the user’s evaluation of the item. Since the user evaluation matrix R is approximately equal to X, the loss function can be expressed as(5)LU,M=∑i,j∈TRij−UiMjT2+λUi2+Mi2.The regularization term of the above formula is(6)LU,M=∑i,j∈TRij−UiMjT2+λUi2+Mi2.Every time the characteristics of each user are updated iteratively, fixedly, and one by one, the partial derivative can be solved:(7)Ui=RiMuiTMuiTM+λnuiI−1,i∈1,M.Find the matrix and the way to find the matrix, and then we can get(8)Mj=RjTUmjUmjmTUmj+λmjI−1,j∈1,n.Through the continuous iteration of the above formula, the similarity matrixU of the user evaluation matrix M can be calculated by taking advantage of the matrix R and the transformation matrix of the matrix X.


## 3.3. Digital Value Analysis of Folk Art Resources

The public is gradually becoming aware of the allure of traditional art digitalization. This method of presenting the world with the unique beauty of folk art is based on a digital museum design created by combining art and digital display technology. In the case of traditional folk art, digital display brings the artistic conception closer to life and more “earthy,” gradually shifting the standard of value evaluation of traditional art in previous humanistic aesthetic activities. Simultaneously, it demonstrates the reality of the conjecture of virtual display of traditional art in the real world, as well as providing some reference and inspiration value for people working on digital displays of other traditional aesthetics. In terms of the humanities, folk art is full of life flavor and authentic public aesthetics because it comes directly from the masses of people. From a communication standpoint, the dissemination of folk art resources through digitalization must first be presented to people as works of art. It is also the public’s recognition of these folk arts that is based on browsing the digital dissemination of folk art resources. Furthermore, this can allow people to directly experience the value enhancement of traditional culture as science and technology advances. As one of the art display media with vitality and development potential, digital media is a media culture with social service attribute developed based on human imagination and creativity. Figure3 shows the flowchart of digital processing of folk art.Figure 3
Flowchart of digital processing of folk art.Relying on this, the digital display of folk art emerges as the times require. With its unique form of expression, folk art can win more public support from users for the digital display form on the basis of its own inherent cultural value. Due to the easy dissemination and replicability of cultural digital exhibits, digital technology provides convenient conditions for the creation and transmission of mass art. At the same time, it encourages artworks to be presented in a digital way. To a certain extent, it increases people’s impulse to contact and collect the physical works of art being disseminated in digital media. But there is a need for similarity that is different from people’s traditional cultural aesthetics, and different folk arts are not the same. For the abovementioned similarity problem, this paper proposes to use the calculation of project similarity. As a result, we use Pearson correlation coefficient to calculate the similarity; then,(9)simi,j=∑c∈IijRi,c−R¯iRj,c−R¯j∑c∈IijRi,c−R¯i2∑c∈IijRj,c−R¯j2.The loss function after incorporating the similarity calculation is as follows:(10)fU,M=∑i,j∈TRij−UiMjT2+λUi2+Mi2+∑c∈IijRj,c−R¯iRj,c−R¯j∑c∈IijRi,c−R¯i2∑c∈IijRj,c−R¯j2.Among them, the common evaluation set of useri,j and their two users on the project is Iij,Ri,c, which is the evaluation of user i on the project c, Rj,c is the evaluation of user j on the project c, and R¯i,R¯j is the average evaluation of user i,j on the project XXX, respectively.


## 4. Result Analysis and Discussion

In this paper, the average absolute error (MAE) is used as an evaluation index to measure the quality of the evaluation algorithm, which is commonly used in recommendation systems, as shown in Figure4.Figure 4
Experimental results of root mean square error under different nearest neighbor numbers.On Hadoop distributed computing platform, test the change of MAE value of the improved ALS algorithm under different numbers of nearest neighbors. From Figure4, it can be found that with the increase of the number of users’ nearest neighbors, the MAE value is decreasing, indicating that the error of prediction results is getting smaller and smaller, and the recommendation accuracy is improving. When the number of neighbors is 50, the recommendation accuracy is the highest, and after 50, the MAE value will slowly increase, indicating that the recommendation accuracy begins to decline. The comparison of the indicators as indicated in Figure 5 is determined to be a further enhancement and comparison of the MAE method.Figure 5
Comparison of experimental results of average error.It can be seen from Figure5 that the average error of the improved algorithm on the basic axis is less than that before the improvement. Therefore, this paper can improve the algorithm to obtain the filtered data more accurately and reasonably than the original. It provides an important support for the establishment of the whole system model. If we test the recommendation of folk art resources, then the best similarity correction parameters obtained through experiments need to be calculated with the results of classical algorithms. The data line chart obtained by testing on Hadoop distributed platform and stand-alone environment is shown in Figure 6.Figure 6
arameter comparison chart.It can be seen from Figure6 that the improved similarity adjustment parameter is more accurate in accuracy when the parameter setting must be. The influence of the improved Mae algorithm on the parameters continues in the whole experimental stage, and through calculation, we know that compared with the original algorithm, the overall accuracy of the modified algorithm is improved by 85%. Based on user-based and item-based collaborative filtering algorithms, this paper also compares other related algorithms in the same field for the digital utilization of folk art resources. Figure 7 shows the result of the comparison.Figure 7
MAE comparison of four algorithms at differentK values.Through observation, we know that the MAE value of each algorithm will decrease with the increase ofK. However, in the same case, the MAE of item CF and user CF is the highest. The algorithm proposed in this paper also has a good prediction effect based on the minimum value of the user’s MAE in the experiment. The size of the original experimental data set is divided according to the percentages of 10%, 30%, 50%, 80%, and 100%, and the operating efficiency of the algorithm is tested on three nodes, five nodes, a single machine on a single machine, and a Hadoop distributed platform, respectively. For comparison experiments, the experimental results are shown in Figure 8.Figure 8
Comparison of operating efficiency under different data set sizes.As can be seen from Figure8, when the data set size is 10% smaller, the stand-alone environment runs faster than the Hadoop distributed environment. It can be seen that in the case of less data, the single-machine environment is more efficient, whereas in the case of less data, each node in the Hadoop distributed cluster takes a certain amount of time to start and assign tasks, resulting in more time for the algorithm to run on the Hadoop distributed cluster with less data. The running time of the algorithm in the stand-alone environment is much longer than in the Hadoop distributed cluster when the amount of data is large, as shown in the latter part of the figure, and this difference becomes increasingly noticeable as the amount of data grows. When dealing with large data sets, the advantages of Hadoop distributed computing stand out.The above algorithm examines the flaws in traditional recommendation algorithms, proposes annotating resources using virtual reality technology, then maps user feature labels to resources using user access records, and finally uses the user label model. Based on the cluster results, make collaborative filtering recommendations. Finally, the recommendation algorithm based on user label model clustering is implemented in this paper using the MapReduce distributed computing framework of the Hadoop distributed environment, and the algorithm is tested and verified using experiments. The adjustment parameters of user label and user score in the calculation of user comprehensive similarity were first examined in the experiment, and the optimal parameter value was found to be 0.5 after several trials. Second, when user neighborK is 25, the improved algorithm in this paper is tested to see if it has the best recommendation effect, and the algorithm with the best parameter is compared to other algorithms. The experimental results show that, under certain conditions and scopes, the improved algorithm performs better in the recommendation effect. Finally, the efficiency of the improved algorithm is assessed using experimental data sets of various sizes in both stand-alone and Hadoop distributed environments in this paper.


## 5. Conclusions

The pace of China’s modernization is accelerating, and the original ecological space on which it depends will be further threatened, posing unprecedented challenges to the protection work. The rational application of modern scientific and technological means, such as digital technology and information technology, for the protection of development has become an unavoidable trend. This paper examines and makes use of the digitization of folk art resources using virtual reality technology and a collaborative filtering algorithm. Folk art resources are always extremely complicated and diverse as a result of their rich intangible cultural heritage, so they must be classified and screened. However, based on the previous algorithm, the combination of virtual reality technology does not receive much support and simplification. As a result, a collaborative filtering algorithm-based optimization scheme is proposed in this paper. The adjustment parameters of user tags and user ratings in the user’s comprehensive similarity calculation are first analyzed in the experiment, and the optimal parameter value is determined to be 0.5 after multiple experiments. Second, when the user’s neighborK is 25, the improved algorithm in this paper is tested to see which has the best recommendation effect. The best-parameter algorithm is then compared to others. The improved algorithm in this paper performs better in the recommendation effect under certain conditions and scope, according to the experimental results. The improved similarity adjustment parameters are more accurate in terms of accuracy, and the effect of the improved MAE algorithm on the parameters lasts throughout the entire experimental stage. We know that, when compared to the original algorithm, the modified algorithm’s overall accuracy rate improves by 85%. The collaborative filtering algorithm based on user label model clustering proposed in this paper, on the other hand, has the smallest k-value Mae, indicating that it has a good score prediction effect in the experiment.



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*Source: 8198830-2022-07-01.xml*