# Analysis of Brand Visual Design Based on Collaborative Filtering Algorithm

Authors: Gao Chaomeng; Wang Yonggang
Journal: Discrete Dynamics in Nature and Society
 (2022)

---

## Abstract

With the continuous development of China’s social economy, the competitiveness of brand market is gradually increasing. In order to improve their own level in brand building, major enterprises gradually explore and study visual communication design. Brand visual design has also received more and more attention. Building a complete and rich visual design system can improve the brand level and attract users to consume. Based on the abovementioned situation, this paper proposes to use collaborative filtering algorithm to analyze and study brand visual design. Firstly, a solution is proposed to solve the problem of low accuracy of general recommendation algorithm in brand goods. Collaborative filtering algorithm is used to analyze the visual communication design process of enterprise brand. Research on personalized image design according to consumers’ trust and recognition of brand design is conducted. In traditional craft brand visual design, we mainly study the impact of image design on consumer behavior. The brand loyalty model is used to predict and analyze the visual design effect. Also, the user’s evaluation coefficient is taken as the expression of brand visual design recognition. Finally, the collaborative filtering algorithm is optimized to improve the consumer similarity based on the original algorithm. The results show that the brand visual design using collaborative filtering algorithm can help enterprises obtain greater benefits in their own brand construction. It provides effective data help in the development of traditional craft brands.


---

## Body

## 1. Introduction

Visual communication design can help things communicate visually and realize specific active behaviors and abilities. In the process of communication, most of them use visual way to achieve the purpose (Lin Feng et al., 2021) [1] through logo, color, animation, and other graphic design for dynamic image performance. In our research, we found that visual design is mostly used in media communication (Zhou Mengying et al., 2021) [2]. It has developed rapidly in the education industry, news industry, and fashion design industry. Matching animated images with appropriate words or pictures in the design process can improve the influence and communication ability of things (Ye Fang et al., 2021) [3]. Visual design symbol is the signal of expressing nature that human eyes can see. Some TV works and ancient buildings are all kinds of information conveyed by objects. It can further help recipients obtain information content (Li Guangrong et al., 2021) [4]. The dissemination of visual information can carry out individual personalized recommendation and group recommendation. It is mainly divided into several steps, including visual selection, visual sending, information transmission, receiver acquisition, and communication influence. (Yang Jinling et al., 2021) [5].Brand is a manifestation of an enterprise’s own judgment on the market environment (Qin Xuping et al., 2021) [6]. A good brand effect can bring more economic benefits to enterprises, but now, enterprises generally do not have high awareness of their own brand (Lu XuKe et al., 2021) [7]. Thus, brand building has been in a downturn. Brand visual design can help the brand to replan its style and form an innovative image design. The help of visual design to the brand is mainly to innovate and improve the brand appearance through its own design scheme, graphics, color, etc., then expand consumers’ understanding and love of the brand, and promote the development of the brand and the enterprise. Visual design also plays an important role in brand culture communication (TU Haojie et al., 2021) [8] to be able to obtain a certain position for the brand in the market environment and maintain a good conceptual image. Secondly, it can have a good reputation and influence in advertising sales and media communication. Adding traditional culture to brand visual design can also help carry forward China’s national culture (Wu Qing et al., 2021) [9]. Visual design can maximize the change of brand image, lay the foundation for enterprise development in the market competition, promote the sales of brand goods, and achieve the purpose of effectively attracting users. Brand visual design is a kind of war and publicity without sound. This most primitive marketing method can often help enterprises develop better.This paper is mainly divided into three parts. The first part analyzes the current situation of brand visual design and puts forward the research methods and contents of this paper. The application status of collaborative filtering algorithm in various countries is briefly analyzed. The second part first understands the role of brand visual design in enterprise construction and studies and discusses the main methods of visual design. In brand visual design, collaborative filtering algorithm is used to realize personalized user interest analysis function. The degree of consumer trust and recognition of brand visual design is used to evaluate the design image. Finally, the traditional process brand visual design method is explored and optimized on the basis of collaborative filtering algorithm, and a loyalty model is proposed for brand user analysis. In order to improve the effect of brand visual design, the interest deviation is added as the discrimination coefficient in the calculation of consumer similarity. The third part analyzes the results of collaborative filtering algorithm and optimized algorithm in brand visual design.


## 2. Related Work

Brand is the embodiment of an enterprise spreading its own culture. In brand visual design, it can organically combine concepts such as culture and values and simplify complex and abstract information into understandable symbolic graphics (Tian zongya et al., 2021) [10] and enable consumers to understand brand related information and concepts. In the process of visual design, the brand must have its own aesthetics in order to obtain the recognition and love of consumers in the link of image design. Most consumers find it difficult to understand a brand’s own culture and value. Visual design can help the brand form its own temperament and image. An excellent visual design can reflect the connotation and grade of the brand itself and bring excellent experience and deep impression to users in the process of selling goods (Liu Xiaojun et al., 2021) [11]. For new goods and traditional industrial goods, visual design is the basis for a good start of the brand in the market. Visual design needs to push the brand’s own communication content to the society and users, resulting in a corresponding positive impact (Liu Ming et al., 2021) [12]. Visual design is not rigid. It is updated and innovated at any time with the development of social economy and culture. In order to explore the effect of brand visual design, we use collaborative filtering algorithm to analyze users’ interests and needs. In the process of brand visual design, a personalized recommendation system is formed for consumers to help the brand achieve better communication effect.In order to study the differences of users, Korea selects personalized products for sale among mass customization products (Li Xujun et al., 2021) [13]. In order to pursue customized products that can meet consumers’ psychological expectations, they need to analyze user satisfaction in the production process. The collaborative filtering algorithm is used to recommend the user customized product scheme, and the interest model is filtered twice, so as to finally realize the user hobby analysis function.The school management system in the UK has always been the key direction of applying various algorithms for optimization (Li Xueting et al., 2021) [14]. They found in the automatic management system of suspension and transfer that, in the face of the increasing number of students year by year, the traditional curriculum arrangement system often has time conflict, personal information error, curriculum conflict, and so on. In order to solve this problem, they use user collaborative filtering recommendation algorithm to optimize the curriculum arrangement system.In order to solve the defects of bibliographic recommendation in the library management system and achieve the effect of optimizing bibliographic recommendation management, Japan proposes to use collaborative filtering algorithm for research (Wang Fei et al., 2021) [15]. This paper analyzes the problems and disadvantages in the current library recommendation program and designs the personalized recommendation function. It can synchronize user registration and login to the management system and effectively control the lending and planning of each book. The book recommendation results with high similarity are obtained by collaborative filtering algorithm and finally fed back to users. In the system test results, the collaborative filtering algorithm can ensure the personalized needs of users.The online learning platforms of Chinese universities often fail to meet the needs of students. In order to solve this problem, they built a collaborative filtering algorithm model to recommend students’ learning resources. In the process of practical application, students’ personal attribute characteristics are added to classify and recommend learning resources. Finally, the accuracy and similarity of the model are calculated. The results show that the model can meet the daily use of colleges and universities in terms of personalized needs. According to the application status of collaborative filtering algorithm in various countries, this paper combines it with brand visual design.


## 3. Methodology

### 3.1. Research on Consumer Feedback after Brand Visual Image Design Based on Collaborative Filtering Algorithm

Collaborative filtering algorithm is a well-known and commonly used recommendation algorithm. It finds users’ preferences and preferences based on the mining of users’ historical behavior data, and predicts the products that users may like. We should find rules from the user’s behavior and preferences and give recommendations based on this. Therefore, how to collect user preference information has become the most basic determinant of the system recommendation effect. With more and more kinds of products, the visual design of the brand becomes more and more important. In order to effectively improve brand aesthetics, an enterprise needs to change its image with visual communication as the core. Only by reasonably optimizing the visual design image can we expand the market competitiveness. We take consumers as the main evaluators of design aesthetics to strengthen their influence on the market and competitive advantage. Brand visual design can stimulate consumers’ purchase desire. Consumers’ recognition and love directly determine the value of a brand itself. Good image design can stimulate users’ consumption tendency. For consumers and users, the sense of visual design image can impact the spiritual level, so as to promote consumption desire. Users can get satisfaction and happiness after purchasing carefully designed visual image brand goods. We investigated consumers to explore the impact of good visual design and ordinary visual design on consumers’ purchase demand, as shown in Figure1.Figure 1
The impact of good visual design and ordinary visual design on consumers’ purchase demand.It can be seen from Figure1 that, in the traditional ordinary visual design, most consumers do not form consumption desire. With the emergence of more and more excellent brand designs, many consumers begin to form a purchase tendency, and their evaluation of the brand is also relatively high after the actual consumption link. The visual design of a brand can also reflect the enterprise’s strategy and value concept of future marketing. Visual symbols are relative to cultural symbols, forming a unique brand culture. It shows the concept and core value of brand management and development in the way of visual design and visual image. In the process of improving brand visual design, personalized and unique needs have a great impact on the image system. The brand needs to clarify the image, product positioning, and design style trend and, from the user’s interest, the rational planning and design scheme. The corresponding model is established according to the historical consumption records and characteristic data information of consumers. The collaborative filtering algorithm is used to calculate the set of consumer interest features and finally realize the brand visual design orientation.The coefficient of consumer trust is added to the collaborative filtering algorithm, and the degree of brand recognition is also used as an influencing variable. We combine the effect of brand visual design with consumers’ trust and optimize the brand design tendency on this basis to meet consumers’ personalized needs. In order to achieve the success rate of designing personalized image, we combine the core content of brand marketing and integrate consumer trust relationship with design concept. The system architecture is shown in Figure2.Figure 2
System architecture.As can be seen from Figure2, the whole system architecture is mainly divided into consumer behavior data collection and information interaction modules. Firstly, it analyzes consumers’ purchase records of brand goods and analyzes consumers’ interests and hobbies. Finally, the user activity is calculated to provide evaluation data for the brand visual design scheme. Firstly, the original visual design set of the consumer group and brand is defined as,(1)U=u1,u2,…,un,B=b1,b2,…,bm.We conduct interactive calculation according to consumer behavior data and analyze the record and evaluation coefficient of purchasing brand goods and obtain the recognition coefficient of consumers for the brand and construct the variable:(2)uiBcoi,j,Uaci,j,here, Bcoi,j represents the recognition coefficient of consumers for brand goods, and Uaci,j represents the activity coefficient of consumers during the whole purchase period. In the trust relationship analysis, we need to calculate the classification distance of consumers:(3)UR=d11d12…d1nd21d22…d2n…………dn1dn2…dnn.In the formula,d represents the distance between consumer data elements. In the process of trust calculation, assuming that the communication between each consumer is synchronous, we calculate the relationship matrix as follows:(4)T=Tr11Tr12…Tr1nTr21Tr22…Tr2nTrn1Trn2…Trnn.In the formula,T vector is the trust degree between users. In the selection of consumer proximity, we need to combine the matrix and trust to control the distance within the same range. The recommended effects of brand visual design are as follows:(5)URn=adi1di2din+βTri1Tri2Trin.Finally, the target selection method is used to form a constraint recommendation table between adjacent users. A recommendation function model for the goods designed by the brand is formed. In order to reasonably determine the element value, we need to study and calculate the collaborative filtering algorithm many times to obtain their accuracy coefficient and feedback coefficient. Brand visual design can form a recognition coefficient for consumers, and consumers’ understanding of the brand can also be obtained from image design. How to win the recognition of consumers is the focus of each enterprise. If users are not satisfied with the brand image design, consumers’ acceptance will become less in brand sales. Therefore, in the process of brand visual design, we need to analyze the recognition of the design image. The following formula can be used in the calculation of consumers’ brand recognition:(6)Bcoib=PibPi×logb_mnrPibPb+1,here, Bcoib coefficient represents consumers’ recognition of a brand design, and i represents consumers’ purchase coefficient of brand goods. Finally, we evaluate the consumers’ evaluation level of the purchased brand goods in the visual design and judge the change of brand visual design effect on enterprise influence from positive and negative directions. In the consumer evaluation level, the activity coefficient is added to the evaluation experience:(7)Uaci=1,λ×RliRlmax+1−λ×logCmaxCi+1,Uaci=λ×RliRlmax+1−λ×logCwCWi+1.In the formula,Uaci represents the evaluation coefficient of consumers on brand goods and Rli represents the number of stay visits of most users after brand design innovation. Finally, we calculate the commodity model and consumers’ preference after the change of brand visual design image:(8)TrD,U=∑i=1n∏j=1mXSj−1,SjXj_total.According to the abovementioned formula, we can know the actual influencing factors of brand visual design effect on consumers. In the calculation of collaborative filtering algorithm, it is often disturbed by dynamic data, the situation that results in the reduction of system accuracy and feedback efficiency. We also need to optimize this algorithm based on the original algorithm.


### 3.2. Research on Visual Image Design of Traditional Craft Brand Based on Collaborative Filtering Optimization Algorithm

With the change of the trend of the times, traditional craft brands carry China’s history and culture for thousands of years. This traditional brand has the style and characteristics of Chinese national culture. In the process of brand development, traditional culture has always been the content of no concern. Western craft products have a deep impact on China. Many traditional crafts with a long history are entering a state of decline. How to embody the connotation of traditional craft and traditional culture in brand visual design is our main research content. In recent years, with the upsurge of national culture, the image of traditional crafts has also begun to change people’s cognition and vision. We use a loyalty model to analyze from the research of brand visual design. Loyalty refers to factors such as brand service quality and commodity price. It can promote consumers to have special dependence through brand image and show behavioral characteristics such as preference. In brand visual design, we need to consider many factors such as consumers’ purchase times, repurchase times, and visits to establish an image model. Firstly, the influence coefficients of these three aspects on brand visual design are analyzed, as shown in Figure3.Figure 3
Influence coefficient of three aspects on brand visual design.It can be seen from Figure3 that the tendency of brand visual design has an inseparable relationship with these three aspects. The number of purchases can accurately express the advantages and disadvantages of the brand visual image. We perform optimization calculation based on collaborative filtering algorithm. Firstly, we assume that the consumer set and brand product set are,(9)U=ui,|i=1,2,…,m,I=Ii,|j=1,2,…,n.Here, the loyalty matrix formed by brand visual design can measure the coefficient between consumers and brands:(10)Mp=Pijm×n,Pij=aCij⋅1+logbcij+2logmaxcij+2,Mp=a+β⋅bij⋅1+logbbij+2logmaxbij+2.In the formula,Cij, bCij, bij, and bbij coefficients, respectively, represent the number of views of consumers on the brand design image. Repurchase times and consumption times. Among them, the number of visits to multiple design images of the same traditional craft brand can only be recorded once. According to the calculation results of the formula, we can find that consumers’ browsing times and repurchase times can better show that consumers prefer the brand visual design image. We use the data filling method to calculate the loyalty of the interactive image of consumers:(11)Pij′=a⋅pik⋅plk⋅pij3+b⋅logubcj+2.The formula for calculating consumers’ preference for brand design is,(12)L=IuaIu,here, Iua represents the consumer’s interest coefficient in traditional craft brands. In order to obtain the hobby matrix of the whole group, we need to calculate each line of data to obtain the interest and hobby deviation of the consumer group:(13)Du,v=∑a=1nLua−Lva2,where n represents the number of brand design schemes. Lua represents the consumer group preference coefficient. According to the abovementioned formula, we can obtain consumers’ feedback on the visual design of traditional craft brands. Based on the original collaborative filtering algorithm, it can analyze the behavior characteristics of users more accurately.


## 3.1. Research on Consumer Feedback after Brand Visual Image Design Based on Collaborative Filtering Algorithm

Collaborative filtering algorithm is a well-known and commonly used recommendation algorithm. It finds users’ preferences and preferences based on the mining of users’ historical behavior data, and predicts the products that users may like. We should find rules from the user’s behavior and preferences and give recommendations based on this. Therefore, how to collect user preference information has become the most basic determinant of the system recommendation effect. With more and more kinds of products, the visual design of the brand becomes more and more important. In order to effectively improve brand aesthetics, an enterprise needs to change its image with visual communication as the core. Only by reasonably optimizing the visual design image can we expand the market competitiveness. We take consumers as the main evaluators of design aesthetics to strengthen their influence on the market and competitive advantage. Brand visual design can stimulate consumers’ purchase desire. Consumers’ recognition and love directly determine the value of a brand itself. Good image design can stimulate users’ consumption tendency. For consumers and users, the sense of visual design image can impact the spiritual level, so as to promote consumption desire. Users can get satisfaction and happiness after purchasing carefully designed visual image brand goods. We investigated consumers to explore the impact of good visual design and ordinary visual design on consumers’ purchase demand, as shown in Figure1.Figure 1
The impact of good visual design and ordinary visual design on consumers’ purchase demand.It can be seen from Figure1 that, in the traditional ordinary visual design, most consumers do not form consumption desire. With the emergence of more and more excellent brand designs, many consumers begin to form a purchase tendency, and their evaluation of the brand is also relatively high after the actual consumption link. The visual design of a brand can also reflect the enterprise’s strategy and value concept of future marketing. Visual symbols are relative to cultural symbols, forming a unique brand culture. It shows the concept and core value of brand management and development in the way of visual design and visual image. In the process of improving brand visual design, personalized and unique needs have a great impact on the image system. The brand needs to clarify the image, product positioning, and design style trend and, from the user’s interest, the rational planning and design scheme. The corresponding model is established according to the historical consumption records and characteristic data information of consumers. The collaborative filtering algorithm is used to calculate the set of consumer interest features and finally realize the brand visual design orientation.The coefficient of consumer trust is added to the collaborative filtering algorithm, and the degree of brand recognition is also used as an influencing variable. We combine the effect of brand visual design with consumers’ trust and optimize the brand design tendency on this basis to meet consumers’ personalized needs. In order to achieve the success rate of designing personalized image, we combine the core content of brand marketing and integrate consumer trust relationship with design concept. The system architecture is shown in Figure2.Figure 2
System architecture.As can be seen from Figure2, the whole system architecture is mainly divided into consumer behavior data collection and information interaction modules. Firstly, it analyzes consumers’ purchase records of brand goods and analyzes consumers’ interests and hobbies. Finally, the user activity is calculated to provide evaluation data for the brand visual design scheme. Firstly, the original visual design set of the consumer group and brand is defined as,(1)U=u1,u2,…,un,B=b1,b2,…,bm.We conduct interactive calculation according to consumer behavior data and analyze the record and evaluation coefficient of purchasing brand goods and obtain the recognition coefficient of consumers for the brand and construct the variable:(2)uiBcoi,j,Uaci,j,here, Bcoi,j represents the recognition coefficient of consumers for brand goods, and Uaci,j represents the activity coefficient of consumers during the whole purchase period. In the trust relationship analysis, we need to calculate the classification distance of consumers:(3)UR=d11d12…d1nd21d22…d2n…………dn1dn2…dnn.In the formula,d represents the distance between consumer data elements. In the process of trust calculation, assuming that the communication between each consumer is synchronous, we calculate the relationship matrix as follows:(4)T=Tr11Tr12…Tr1nTr21Tr22…Tr2nTrn1Trn2…Trnn.In the formula,T vector is the trust degree between users. In the selection of consumer proximity, we need to combine the matrix and trust to control the distance within the same range. The recommended effects of brand visual design are as follows:(5)URn=adi1di2din+βTri1Tri2Trin.Finally, the target selection method is used to form a constraint recommendation table between adjacent users. A recommendation function model for the goods designed by the brand is formed. In order to reasonably determine the element value, we need to study and calculate the collaborative filtering algorithm many times to obtain their accuracy coefficient and feedback coefficient. Brand visual design can form a recognition coefficient for consumers, and consumers’ understanding of the brand can also be obtained from image design. How to win the recognition of consumers is the focus of each enterprise. If users are not satisfied with the brand image design, consumers’ acceptance will become less in brand sales. Therefore, in the process of brand visual design, we need to analyze the recognition of the design image. The following formula can be used in the calculation of consumers’ brand recognition:(6)Bcoib=PibPi×logb_mnrPibPb+1,here, Bcoib coefficient represents consumers’ recognition of a brand design, and i represents consumers’ purchase coefficient of brand goods. Finally, we evaluate the consumers’ evaluation level of the purchased brand goods in the visual design and judge the change of brand visual design effect on enterprise influence from positive and negative directions. In the consumer evaluation level, the activity coefficient is added to the evaluation experience:(7)Uaci=1,λ×RliRlmax+1−λ×logCmaxCi+1,Uaci=λ×RliRlmax+1−λ×logCwCWi+1.In the formula,Uaci represents the evaluation coefficient of consumers on brand goods and Rli represents the number of stay visits of most users after brand design innovation. Finally, we calculate the commodity model and consumers’ preference after the change of brand visual design image:(8)TrD,U=∑i=1n∏j=1mXSj−1,SjXj_total.According to the abovementioned formula, we can know the actual influencing factors of brand visual design effect on consumers. In the calculation of collaborative filtering algorithm, it is often disturbed by dynamic data, the situation that results in the reduction of system accuracy and feedback efficiency. We also need to optimize this algorithm based on the original algorithm.


## 3.2. Research on Visual Image Design of Traditional Craft Brand Based on Collaborative Filtering Optimization Algorithm

With the change of the trend of the times, traditional craft brands carry China’s history and culture for thousands of years. This traditional brand has the style and characteristics of Chinese national culture. In the process of brand development, traditional culture has always been the content of no concern. Western craft products have a deep impact on China. Many traditional crafts with a long history are entering a state of decline. How to embody the connotation of traditional craft and traditional culture in brand visual design is our main research content. In recent years, with the upsurge of national culture, the image of traditional crafts has also begun to change people’s cognition and vision. We use a loyalty model to analyze from the research of brand visual design. Loyalty refers to factors such as brand service quality and commodity price. It can promote consumers to have special dependence through brand image and show behavioral characteristics such as preference. In brand visual design, we need to consider many factors such as consumers’ purchase times, repurchase times, and visits to establish an image model. Firstly, the influence coefficients of these three aspects on brand visual design are analyzed, as shown in Figure3.Figure 3
Influence coefficient of three aspects on brand visual design.It can be seen from Figure3 that the tendency of brand visual design has an inseparable relationship with these three aspects. The number of purchases can accurately express the advantages and disadvantages of the brand visual image. We perform optimization calculation based on collaborative filtering algorithm. Firstly, we assume that the consumer set and brand product set are,(9)U=ui,|i=1,2,…,m,I=Ii,|j=1,2,…,n.Here, the loyalty matrix formed by brand visual design can measure the coefficient between consumers and brands:(10)Mp=Pijm×n,Pij=aCij⋅1+logbcij+2logmaxcij+2,Mp=a+β⋅bij⋅1+logbbij+2logmaxbij+2.In the formula,Cij, bCij, bij, and bbij coefficients, respectively, represent the number of views of consumers on the brand design image. Repurchase times and consumption times. Among them, the number of visits to multiple design images of the same traditional craft brand can only be recorded once. According to the calculation results of the formula, we can find that consumers’ browsing times and repurchase times can better show that consumers prefer the brand visual design image. We use the data filling method to calculate the loyalty of the interactive image of consumers:(11)Pij′=a⋅pik⋅plk⋅pij3+b⋅logubcj+2.The formula for calculating consumers’ preference for brand design is,(12)L=IuaIu,here, Iua represents the consumer’s interest coefficient in traditional craft brands. In order to obtain the hobby matrix of the whole group, we need to calculate each line of data to obtain the interest and hobby deviation of the consumer group:(13)Du,v=∑a=1nLua−Lva2,where n represents the number of brand design schemes. Lua represents the consumer group preference coefficient. According to the abovementioned formula, we can obtain consumers’ feedback on the visual design of traditional craft brands. Based on the original collaborative filtering algorithm, it can analyze the behavior characteristics of users more accurately.


## 4. Result Analysis and Discussion

### 4.1. Analysis of Consumer Feedback after Brand Visual Image Design Based on Collaborative Filtering Algorithm

In the development of brands and enterprises, personalized visual image has become the main direction for brands to expand market scale. In order to clarify the direction of brand visual design and product positioning, we need to study the feedback evaluation of consumers and, at the same time, in the process of exploring visual design, improve their own aesthetics and expand the audience and guide users to have a deeper understanding of our brand connotation and concept. Showing the particularity of the brand in the visual design image is the main way to distinguish it from other brands. This unique charm has laid a solid foundation for the development of the brand. Consumers can also increase the interaction with the brand side in the process of feeding back the brand visual image, so as to achieve a win-win situation. We use collaborative filtering algorithm to calculate the brand visual design image and consumer trust. The algorithm applied in the research environment is programming language. Firstly, after obtaining the visual design image of a brand through data mining, the evaluation coefficient of consumers changes, as shown in Figure4.Figure 4
Consumer evaluation coefficient before and after brand visual design.It can be seen from Figure4 that consumers’ purchasing power is low before brand visual design. As the image after the brand design continues to penetrate the masses, everyone’s evaluation index of brand goods is also gradually increasing. In order to verify the effectiveness of this algorithm, we also compare it with other algorithms. The accuracy of the algorithm is compared and judged under the same data environment. The comparison coefficient is divided into consumer evaluation, consumer evaluation content, consumer purchase price, and other directions. Finally, it is concluded that the collaborative filtering algorithm can accurately analyze the effect of brand visual design according to consumers’ interests and trust coefficient.


### 4.2. Analysis of Research Results of Traditional Process Brand Visual Image Design Based on Collaborative Filtering Optimization Algorithm

With the rapid development of economy and society, people’s demand for living materials is higher and higher. The traditional material needs can no longer meet the modern social environment. People’s pursuit of high quality of spiritual life is more intense, and the dissemination of national culture has become an important part of life. From the perspective of clothing brands, under the influence of western culture, our country’s brand design was in the imitation stage at the beginning. With the revival of national culture, our traditional craft and traditional culture have gradually formed a new corporate culture and brand design. The literature related to traditional brand visual design gradually increases with the year. This also shows that our direction of brand visual image design is not confined to a certain field. At present, there are still many deficiencies in brand visual image design. How to combine consumers’ interests and hobbies for visual design is our main concern. Therefore, we use collaborative filtering algorithm to realize personalized recommendation of brand visual design. Using the characteristics of consumers’ interests and hobbies as the evaluation coefficient, this paper analyzes whether the main elements added in the brand visual design can meet the personalized needs of consumers, so as to realize the guidance of visual design. The brand loyalty coefficient can reflect the service quality brought by visual design image to consumers. It can also judge whether there is unreasonable price in the process of brand sales. According to consumers’ dependence on the brand after visual design, we can analyze whether the image design is successful. The traditional collaborative filtering algorithm has some problems such as interest deviation in consumer similarity calculation. We optimize the algorithm to improve the accuracy and feedback efficiency of similarity calculation. We compare the performance of the traditional algorithm and the optimized algorithm, as shown in Figure5.Figure 5
Comparison of feedback efficiency between traditional algorithm and optimized algorithm.As can be seen from Figure5, with the increase of brand consumers, the traditional algorithms can no longer effectively transmit consumers’ feedback. The optimized collaborative filtering algorithm ensures the computational feedback efficiency. Finally, according to the evaluation content of consumers, we put forward some suggestions on the visual design scheme of traditional craft brand. First of all, we should not blindly use national culture and ancient style elements, which will lead to the lack of identification of the image of brand design. We need to improve the concept of brand personalized design and reduce the problem of high similarity. In brand image design, visual symbols cannot be abused. We should combine brand image with visual symbols to improve consumers’ and audience’s understanding of brand image. Finally, in the brand packaging design, we should also continue the effect of visual communication and adopt unique environmental protection packaging and match the packaging design with the brand to improve the fit of the whole brand.


## 4.1. Analysis of Consumer Feedback after Brand Visual Image Design Based on Collaborative Filtering Algorithm

In the development of brands and enterprises, personalized visual image has become the main direction for brands to expand market scale. In order to clarify the direction of brand visual design and product positioning, we need to study the feedback evaluation of consumers and, at the same time, in the process of exploring visual design, improve their own aesthetics and expand the audience and guide users to have a deeper understanding of our brand connotation and concept. Showing the particularity of the brand in the visual design image is the main way to distinguish it from other brands. This unique charm has laid a solid foundation for the development of the brand. Consumers can also increase the interaction with the brand side in the process of feeding back the brand visual image, so as to achieve a win-win situation. We use collaborative filtering algorithm to calculate the brand visual design image and consumer trust. The algorithm applied in the research environment is programming language. Firstly, after obtaining the visual design image of a brand through data mining, the evaluation coefficient of consumers changes, as shown in Figure4.Figure 4
Consumer evaluation coefficient before and after brand visual design.It can be seen from Figure4 that consumers’ purchasing power is low before brand visual design. As the image after the brand design continues to penetrate the masses, everyone’s evaluation index of brand goods is also gradually increasing. In order to verify the effectiveness of this algorithm, we also compare it with other algorithms. The accuracy of the algorithm is compared and judged under the same data environment. The comparison coefficient is divided into consumer evaluation, consumer evaluation content, consumer purchase price, and other directions. Finally, it is concluded that the collaborative filtering algorithm can accurately analyze the effect of brand visual design according to consumers’ interests and trust coefficient.


## 4.2. Analysis of Research Results of Traditional Process Brand Visual Image Design Based on Collaborative Filtering Optimization Algorithm

With the rapid development of economy and society, people’s demand for living materials is higher and higher. The traditional material needs can no longer meet the modern social environment. People’s pursuit of high quality of spiritual life is more intense, and the dissemination of national culture has become an important part of life. From the perspective of clothing brands, under the influence of western culture, our country’s brand design was in the imitation stage at the beginning. With the revival of national culture, our traditional craft and traditional culture have gradually formed a new corporate culture and brand design. The literature related to traditional brand visual design gradually increases with the year. This also shows that our direction of brand visual image design is not confined to a certain field. At present, there are still many deficiencies in brand visual image design. How to combine consumers’ interests and hobbies for visual design is our main concern. Therefore, we use collaborative filtering algorithm to realize personalized recommendation of brand visual design. Using the characteristics of consumers’ interests and hobbies as the evaluation coefficient, this paper analyzes whether the main elements added in the brand visual design can meet the personalized needs of consumers, so as to realize the guidance of visual design. The brand loyalty coefficient can reflect the service quality brought by visual design image to consumers. It can also judge whether there is unreasonable price in the process of brand sales. According to consumers’ dependence on the brand after visual design, we can analyze whether the image design is successful. The traditional collaborative filtering algorithm has some problems such as interest deviation in consumer similarity calculation. We optimize the algorithm to improve the accuracy and feedback efficiency of similarity calculation. We compare the performance of the traditional algorithm and the optimized algorithm, as shown in Figure5.Figure 5
Comparison of feedback efficiency between traditional algorithm and optimized algorithm.As can be seen from Figure5, with the increase of brand consumers, the traditional algorithms can no longer effectively transmit consumers’ feedback. The optimized collaborative filtering algorithm ensures the computational feedback efficiency. Finally, according to the evaluation content of consumers, we put forward some suggestions on the visual design scheme of traditional craft brand. First of all, we should not blindly use national culture and ancient style elements, which will lead to the lack of identification of the image of brand design. We need to improve the concept of brand personalized design and reduce the problem of high similarity. In brand image design, visual symbols cannot be abused. We should combine brand image with visual symbols to improve consumers’ and audience’s understanding of brand image. Finally, in the brand packaging design, we should also continue the effect of visual communication and adopt unique environmental protection packaging and match the packaging design with the brand to improve the fit of the whole brand.


## 5. Conclusions

With the spread of corporate culture more and more diversified, visual communication design provides a very important help in brand building. Brand image design is not only the display of corporate culture but also the embodiment of external image. Brand visual design can help enterprises obtain certain influence and image status in the corresponding market. Improving brand visual design can promote commodity sales and obtain people’s favor. In the traditional brand visual design, the degree of grasping the similarity of users is low, and many brands still choose to blindly follow the trend of design. Based on the abovementioned situation, this paper proposes to use collaborative filtering algorithm to study and analyze brand visual design. Firstly, it analyzes the role of visual design in brand image using the data to obtain consumers’ views and evaluation coefficients on brand design. Collaborative filtering algorithm is used to calculate the similarity of users and eliminate the interference factors caused by similarity and realize the recommendation of consumers’ personalized characteristics in brand visual design. After the brand visual design, we analyze the feedback coefficient of consumers, optimize the original algorithm, and improve the accuracy and feedback efficiency of the algorithm. Finally, in the traditional process brand visual design, the optimization algorithm is used to study consumer loyalty. The effect of brand visual image design is related to consumer loyalty, and the main factors affecting consumer behavior are explored. The results show that collaborative filtering algorithm can accurately distinguish groups with high similarity, obtain consumers’ personalized interests and hobbies, and integrate them into brand visual design. We get accurate evaluation results in consumer feedback evaluation, which provides help for the direction of brand visual design. However, there are still some problems in this algorithm. For example, a commodity that is very popular for a period of time or a general commodity is purchased by a large number of people. At this time, if it is included in the normal calculation process, it will not make much sense and will increase the burden. This commodity price can be given a weight or treated as dirty data in the data preprocessing stage. This needs further verification in future research.



---

*Source: 8235966-2022-01-13.xml*