# Decision-Making and Management Method of Public Cultural Service Consumption Preference Based on Multisource Big Data Fusion

Authors: Baojing Zhong; Chuan Zhang; Bo Li
Journal: Mathematical Problems in Engineering
 (2022)

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## Abstract

Public cultural service is a product of a new era, and it is also an important product of the urbanization process. Public cultural services can not only improve the cohesion between the government and residents, but also it can improve the living standards and happiness index of residents. The public cultural service product is not only a symbol of the city, and it is more important to meet the needs and satisfaction of the residents. It needs to truly understand the preferences and needs of residents and then build public cultural service products according to their preferences. The social public cultural service model under the traditional model is dominated by the willingness of the government, which makes it difficult to truly understand the preferences and needs of residents. This paper uses data fusion and neural network methods to study the influencing factors in public cultural services. The results show that the data fusion method can capture the relationship between the factors of the public cultural service and the residents' preference, and the error of the four factors is less than 2.83% for the classification of the public cultural service. The largest prediction error is only 3.11%, and this part of the error comes from residents' satisfaction with the public cultural industry, which is an acceptable error.


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## Body

## 1. Introduction

In the twentieth century, people's way of life and living standards are relatively low, and public cultural services have not been universal [1]. However, along with the economic level and the national comprehensive strength promotion, the people living standards and the quality of life obtained a very big promotion. The public cultural service has developed rapidly, which is also an important symbol of the improvement of the living standard of the residents [2–4]. Similarly, with the rapid development of urbanization and new rural construction, the public cultural service has been further developed. Public cultural service products have appeared in many types, such as public sports facilities, landscape gardens, public entertainment places, etc. To a certain extent, it meets the living standards of residents and the level of physical exercise. When the public cultural service appears in the construction of a city or a new countryside, it can not only encourage residents to participate in the pursuit of a higher standard of living but also can close the living distance between people [5–7]. In the process of urbanization, people are often under the pressure of life, such as car loans and mortgages. This has led to tremendous stress in people's lives and a decline in their physical and mental health. On the surface, urbanization has improved residents' quality of life, but it has also reduced residents' happiness [8]. The emergence of public cultural service products is not only a task of the government. Its most important aim is to improve the physical and mental level and happiness index of residents. It can also enrich the residents' life style. The public cultural service industry has become a part of people’s lives, which is also an important task of government departments. The research on the public cultural service industry is of great significance to the life and tasks of the residents and the government.The public cultural service was originally an official task of the government, which aimed to improve the quality of life and happiness of residents. However, with the continuous development of the public cultural service, it has deviated from its original intention [9, 10]. The public cultural service should take the residents’ preference and demand as the starting point and then build corresponding public cultural service products according to local policies and economic levels. The development of the public cultural service industry can reflect the cultural characteristics and local economic characteristics of a city or new countryside to a certain extent [11]. However, it cannot carry out public cultural service products only based on the government’s intention and the requirements of the government's achievements. If a public cultural service product cannot well satisfy the preference and satisfaction of local residents, it will result in a waste of public cultural service products [12–14]. To a certain extent, it also wastes local government finances. A successful public cultural service product can not only improve residents’ living standards and happiness index, but also it can stimulate residents' motivation to create more financial income for the local area [15]. However, there are many influencing factors in the construction of public cultural service products, and the complex influencing factors limit the decision-making of government staff. Government workers may only refer to successful public cultural service cases, which often ignore the needs of local residents and the local economic level. The public cultural service is a complex task. It not only needs to build corresponding products according to residents’ satisfaction and economic level, but also it needs higher science and technology to support this task.Data fusion technology is also an important part of big data technology, which can well mine and predict complex data [16]. Data fusion technology can be competent in mining the data relationship between various influencing factors of public cultural service products. In the early stage of the development of big data technology, it can only process some simple tasks and a small amount of data. However, with the continuous improvement of economic level and living types, the amount of data in each field has skyrocketed exponentially, which requires more complex big data algorithms and higher-performance computers to process these data [17]. In the early twenty-first century, the computing power of computers has been improved, and the level of hardware such as GPU has also been improved. This provides a high guarantee for the development of big data technology. For the public cultural service, its data are not only huge in magnitude, but their characteristics are also more complex, whether in spatial or temporal characteristics. If it only relies on simple algorithms to process these data, it may not be able to learn deeper features, which will lead to poor generalization ability of the learning algorithm [18]. The emergence of big data technology provides good technical support for the development of the public cultural service. It can design some types of public cultural services suitable for the local area according to factors such as residents' preference and economic level.The combination of data fusion technology and public cultural service can fully explore the relationship between the influencing factors of public cultural service. Data fusion technology can use data fusion technology to learn more feature relationships from successful public service cultural industries, and then it can learn more feature relationships [19]. It can further provide a guiding reference for the development of local public cultural services. There is a relatively large relationship between the public cultural service and local residents' preferences, satisfaction, economic level, and local policies [20]. This task only relies on relevant government staff to find the relationship, which is a tricky task. Data fusion technology provides good technical support for the development of the public cultural service. Big data technology can help researchers in the public cultural industry to obtain more potential information and relevant information. The advantage of big data technology is to process massive data. With the continuous development of the public service industry, it has produced a large amount of data. Big data technology can help government workers process this data and it can find data among relationships.The purpose of this research is to use data fusion technology and neural network method to fully explore the correlation of influencing factors in the public cultural service. This paper is mainly divided into 5 parts. The first part introduces the development of the public cultural service and the advantages of the data fusion method. The second part mainly introduces the development status of the public cultural service. The third part describes the application method of data fusion technology and neural network method in the public cultural service. The fourth part is the focus of this research, which mainly analyzes the feasibility of the data fusion method and neural network method in predicting the influencing factors of the public cultural service. In Section4, it analyzes the accuracy of the clustering method using the classification criterion graph and mean error. It also analyzes the accuracy of ConvLSTM in predicting public cultural industries using linear correlation coefficients and mean errors and line graphs. The fifth part is the summary part of the article.


## 2. Related Work

For the public cultural service, traditional government decision-making schemes can no longer satisfy residents' preferences. Many researchers have fully researched and analyzed the public cultural service. Wang et al. [21] believed that the public cultural service can contribute to the sustainable development of a country and the world. He used the data envelopment analysis method and the slack-based measurement SBM model to study the impact of public cultural services on national economies. The results showed that GDP, education level, and fiscal decentralization all affect the development of the public cultural service industry. Zhang et al. [22] used user satisfaction and feature approximation collaborative data fusion algorithm to study residents' satisfaction in the community public cultural service industry. He uses the time-varying weight method to improve the TOPSIS method to fuse user similar data. At the same time, he established a performance evaluation model between the public cultural service and residents' satisfaction. The results verify the effectiveness of his proposed algorithm. Nusawat et al. [23] have systematically evaluated the online public service industry and assessed passenger satisfaction based on augmented reality and Internet methods. This network evaluation model is developed based on the WordPress program. The results show that the augmented reality network public service industry can display a new and interesting platform for the development of cultural tourism, and the system stability is relatively high, and passenger satisfaction is relatively high. Tan et al. [24] believed that residents' perception of the cultural tourism ecosystem CES is not very clear. He used questionnaires and structural equation modeling (SEM) to study the influencing factors of CES and residents' perceptions of CES. He suggested that managers should take residents' perceptions into account in the management of CES, and the success of the CES restoration improved residents' perceptions and satisfaction. The public cultural service capacity will affect the public service capacity. Getha-Taylor et al. [25] used Longoria and Rangarajan method to develop the relationship between the public cultural services industry and the people. Wang et al. [26] study the drain behavior of rural resident digital culture and services (RPDCS) and proposes strategies to avoid the drain of rural public cultural industry users. He used least squares and structural equation modeling to study the drain on public cultural services. The result shows that his method can provide good technical support for the managers of the public cultural service industry in order to avoid the loss of users. Xu et al. [27] took the landscape corridor of the silk road as a case study, and it analyzed the impact of resident values on the development of these public cultural projects. He used the spatial pattern approach to analyze residents' perceptions of the silk road landscape. The results showed that the aesthetic value and the value of cultural heritage will be affected by the residents. The riverside was the most popular place among the masses. From the above literature review, it can be seen that researchers have carried out many studies on the public cultural service, and they have used many mathematical models. The research focuses on the relationship between public cultural service and residents’ satisfaction. However, current research rarely uses data fusion and neural network methods to study the relationship between public cultural service and residents satisfaction. This study mainly uses data fusion and neural network methods to study the prediction accuracy among the four influencing factors of the public cultural service, such as residents' satisfaction, residents' needs for public cultural service products, etc. Compared with the existing research, this study uses the relevant big data technology of classification and prediction to study the relevant factors of the public cultural service industry, which combines the satisfaction of residents and the preference of residents well. This study uses the ConvLSTM neural network method to study the relevant characteristics of the public cultural industry, which also considers the temporal characteristics of the public cultural service industry. This is different from other studies.


## 3. The Application of Data Fusion Method in the Management of the Public Cultural Service

### 3.1. The Overview of Data Fusion Technology

Data fusion technology is an important algorithm in the development of big data technology, which can handle the correlation between complex data well [28, 29]. Since the development of public cultural service products can not only rely on the decision-making of government managers, it needs to better approach residents’ preferences and needs [30]. Successful cases of the public cultural service can provide more references for government workers, and the relationship between these complex data needs data fusion technology to process. With the advent of the era of intelligence, the algorithm of data fusion has developed rapidly, and it can be easily selected by government workers. Data fusion technology is a method of feature fusion of different types and categories of data to predict the required features. It includes neural network methods, Kalman filtering methods, and clustering algorithms, and its scope is relatively broad [31].


### 3.2. Introduction of Clustering Method and the Application Process of Data Fusion Technology in the Public Cultural Service

Clustering is a kind of machine learning algorithm, which can be classified according to the feature distance or feature density between data, and it has been successfully applied in many fields. Figure1 shows the working schematic of the clustering method. This paper chooses the distance-based classification working method according to the data types and task requirements of the public cultural service. The ultimate purpose of the clustering method is to divide complex data sets into different categories, and the feature categories of the same distance are as close as possible. The classification between different features is kept as distinct as possible. Figure 1 is just a schematic diagram of the classification principles of the clustering method. The number of classifications selected in this paper is 4. It mainly divides the influencing factors of the public cultural service into four categories: residents satisfaction, residents needs, government policies, and the government economic level.Figure 1
The working rules of clustering methods.This study uses data fusion technology and neural network method to classify and predict the preference of public cultural service products, which mainly includes two processes: data classification and neural network prediction [32]. Figure 2 shows the application process of the data fusion method in the public cultural service using multisource data. This task requires the collection of population satisfaction, population needs, government policies, and government economic levels as a source of multisource data. The data then needs to be processed through a data preprocessing process into equally distributed data sources. In the first step, these multisource data need to be divided into 4 categories according to the feature distance relationship by the clustering method. In the second step, the classified data needs a neural network algorithm to predict the public cultural service. Once the model is trained, government workers can make predictions about unknown public cultural services based on the model weight and bias. The study will help government workers to tailor construction to resident preferences and manage public cultural services. The input data required in this paper mainly include residents' satisfaction, residents' needs, government economic level, and government decision-making, which mainly consists of four parts. This part of the data comes from the public cultural service data of a community in Shanghai [33].Figure 2
The application process of clustering and neural network in the public cultural service industry.The clustering method is similar to the neural network method, and it also has an error propagation function. Equation (1) shows the operation rules of the error function of clustering. In this study, the mean square error function is chosen as the error function of the clustering method.(1)J=12∑k=1l∑r=1oxk−χr2.The operation of the error function is actually a gradient minimization process, which is also the process of finding the optimal weight. Equation (2) shows the rule of finding the optimal value of clustering by gradient descent method. It can find the best mean value of the cluster center.(2)∂J∂χJ=−2∑k=1Njxk−χr.In this study, we choose a distance-based clustering method, which is based on the proximity relationship between the feature data to find a suitable clustering center value. Equation (3) shows the rules of Euclidean distance, a common metric used in clustering. It also takes the form of a mean square error function.(3)dicted=∑k=1mxik−xjk2.Chebyshev distance is also a commonly used distance indicator in clustering, which is a loss function that is passed by taking a limit. Equation (4) shows how the Chebyshev distance works. Like Euclidean distance, it takes the form of a mean square error loss function.(4)dictcd=limt⟶∞∑k=1mxjk−yikl1/l.


### 3.3. Application Method of ConvLSTM Algorithm in the Prediction of the Public Cultural Service

The ConvLSTM algorithm is a new algorithm that combines the advantages of the convolutional neural network and the long short-term memory (LSTM) recurrent neural network [34]. The data of the public cultural service industry will be input into the ConvLSTM neural network in the form of time series. This algorithm needs to set the sliding window size and sliding step size, which can generate corresponding label data. It converts the click operation of the LSTM method into a convolution operation. Figure 3 shows the workflow of the ConvLSTM algorithm in the prediction of the public cultural service. The hyperparameters of ConvLSTM are crucial to the training process of the public cultural service industry. The number of network layers used in this study is 5, and the learning rate is 0.001. This learning rate can well ensure the decline speed of training, and it can avoid gradient diffusion, etc. The stride size used in this study is 3. The factor of the fully connected layer is 256. This algorithm can not only propose the spatial characteristics of the research object, but it can also consider the temporal characteristics of the research object. This algorithm is a neural network that can memorize historical state information. The convolutional neural network only considers the spatial characteristics of the research object, but for the public cultural service, their characteristics have obvious time characteristics, such as economic level and government policy characteristics will change greatly with time. If the algorithm cannot take into account the time characteristics of the public cultural service, it is likely to cause inaccuracy of the algorithm. In order to fully consider the temporal and spatial characteristics of the influencing factors of the public cultural service, the ConvLSTM algorithm was adopted in this study. In this study, the data of the relevant characteristics of the public cultural service industry are processed into the form of time series, which will be input into the ConvLSTM network. It determines the label data according to the sliding window and the sliding step size.Figure 3
The ConvLSTM method in the process of multisource data mapping in public cultural service.Neural network methods all use the derivation operation method because the gradient method is a derivation operation process. In the deep learning algorithm framework, the software will automatically realize the automatic differentiation function. Equations (5) and (6) show the derivation process of the weights and biases of each hidden layer.(5)Δωji=−η∂E∂ωji,(6)Δuij=−η∂E∂∂uij.There are many hyperparameters involved in the convolution operation, which are also parameters that need to be continuously adjusted during the training process. Equation (7) shows the calculation rules for hyperparameters.(7)w′=w+2p−ks+1.The ConvLSTM algorithm has the ability to retain historical state information, which is mainly due to the existence of the gate structure. Equation (8) shows the operation rule of the forget gate, which can selectively retain and forget historical information.(8)αlt=∑t=1kωilxit+∑b=1vωctsbt−1.The input gate is the structure of the input data of the ConvLSTM algorithm. It can jointly input the current state data and historical state information to assign different weights. Equations (9) and (10) show the operation rules of the input gate and activation function.(9)it=σWxi∗xt+Whi∗ht−1+Wci∘Ct−1+bi,(10)ft=σWxf∗xt+Whf∗ht−1+Wcf∘Ct−1+bf.Equation (11) shows the operation of the refresh variable.(11)Ct=ft∘Ct−1+it∘ELUWxc∗xt+Whc∗ht−1+bc.Equation (12) shows the operation rule of the output gate, which will output the current state data and historical state data after the nonlinear operation by weight distribution.(12)ot=σWxo∗xt+Who∗ht−1+Wco∘Ct+bo.


### 3.4. Process Flow of Multisource Data in the Public Cultural Service

Both the data fusion method and the neural network method use the weight and bias distribution method to perform the gradient descent operation. This process requires the distribution and magnitude of the data set to be consistent. Otherwise, if the distribution of the data set is uneven, it is easy to cause uneven weight distribution, which may lead to overfitting. The phenomenon of overfitting is that the accuracy of the training set is good, but the performance of the test set will be poor. For the multisource data of the public cultural service, it is even more necessary to preprocess the data of the training set and the test set. The source and form of multisource data will be very different. Excessive data will cause more weight distribution, which will further lead to the model only performing better on features with larger data sets. Therefore, this study adopts the normalization method to preprocess the multisource data sets. The data preprocessing process of this study adopts the normalization method, and the relevant data of the public cultural service industry will be processed into data that conforms to the normal distribution.


## 3.1. The Overview of Data Fusion Technology

Data fusion technology is an important algorithm in the development of big data technology, which can handle the correlation between complex data well [28, 29]. Since the development of public cultural service products can not only rely on the decision-making of government managers, it needs to better approach residents’ preferences and needs [30]. Successful cases of the public cultural service can provide more references for government workers, and the relationship between these complex data needs data fusion technology to process. With the advent of the era of intelligence, the algorithm of data fusion has developed rapidly, and it can be easily selected by government workers. Data fusion technology is a method of feature fusion of different types and categories of data to predict the required features. It includes neural network methods, Kalman filtering methods, and clustering algorithms, and its scope is relatively broad [31].


## 3.2. Introduction of Clustering Method and the Application Process of Data Fusion Technology in the Public Cultural Service

Clustering is a kind of machine learning algorithm, which can be classified according to the feature distance or feature density between data, and it has been successfully applied in many fields. Figure1 shows the working schematic of the clustering method. This paper chooses the distance-based classification working method according to the data types and task requirements of the public cultural service. The ultimate purpose of the clustering method is to divide complex data sets into different categories, and the feature categories of the same distance are as close as possible. The classification between different features is kept as distinct as possible. Figure 1 is just a schematic diagram of the classification principles of the clustering method. The number of classifications selected in this paper is 4. It mainly divides the influencing factors of the public cultural service into four categories: residents satisfaction, residents needs, government policies, and the government economic level.Figure 1
The working rules of clustering methods.This study uses data fusion technology and neural network method to classify and predict the preference of public cultural service products, which mainly includes two processes: data classification and neural network prediction [32]. Figure 2 shows the application process of the data fusion method in the public cultural service using multisource data. This task requires the collection of population satisfaction, population needs, government policies, and government economic levels as a source of multisource data. The data then needs to be processed through a data preprocessing process into equally distributed data sources. In the first step, these multisource data need to be divided into 4 categories according to the feature distance relationship by the clustering method. In the second step, the classified data needs a neural network algorithm to predict the public cultural service. Once the model is trained, government workers can make predictions about unknown public cultural services based on the model weight and bias. The study will help government workers to tailor construction to resident preferences and manage public cultural services. The input data required in this paper mainly include residents' satisfaction, residents' needs, government economic level, and government decision-making, which mainly consists of four parts. This part of the data comes from the public cultural service data of a community in Shanghai [33].Figure 2
The application process of clustering and neural network in the public cultural service industry.The clustering method is similar to the neural network method, and it also has an error propagation function. Equation (1) shows the operation rules of the error function of clustering. In this study, the mean square error function is chosen as the error function of the clustering method.(1)J=12∑k=1l∑r=1oxk−χr2.The operation of the error function is actually a gradient minimization process, which is also the process of finding the optimal weight. Equation (2) shows the rule of finding the optimal value of clustering by gradient descent method. It can find the best mean value of the cluster center.(2)∂J∂χJ=−2∑k=1Njxk−χr.In this study, we choose a distance-based clustering method, which is based on the proximity relationship between the feature data to find a suitable clustering center value. Equation (3) shows the rules of Euclidean distance, a common metric used in clustering. It also takes the form of a mean square error function.(3)dicted=∑k=1mxik−xjk2.Chebyshev distance is also a commonly used distance indicator in clustering, which is a loss function that is passed by taking a limit. Equation (4) shows how the Chebyshev distance works. Like Euclidean distance, it takes the form of a mean square error loss function.(4)dictcd=limt⟶∞∑k=1mxjk−yikl1/l.


## 3.3. Application Method of ConvLSTM Algorithm in the Prediction of the Public Cultural Service

The ConvLSTM algorithm is a new algorithm that combines the advantages of the convolutional neural network and the long short-term memory (LSTM) recurrent neural network [34]. The data of the public cultural service industry will be input into the ConvLSTM neural network in the form of time series. This algorithm needs to set the sliding window size and sliding step size, which can generate corresponding label data. It converts the click operation of the LSTM method into a convolution operation. Figure 3 shows the workflow of the ConvLSTM algorithm in the prediction of the public cultural service. The hyperparameters of ConvLSTM are crucial to the training process of the public cultural service industry. The number of network layers used in this study is 5, and the learning rate is 0.001. This learning rate can well ensure the decline speed of training, and it can avoid gradient diffusion, etc. The stride size used in this study is 3. The factor of the fully connected layer is 256. This algorithm can not only propose the spatial characteristics of the research object, but it can also consider the temporal characteristics of the research object. This algorithm is a neural network that can memorize historical state information. The convolutional neural network only considers the spatial characteristics of the research object, but for the public cultural service, their characteristics have obvious time characteristics, such as economic level and government policy characteristics will change greatly with time. If the algorithm cannot take into account the time characteristics of the public cultural service, it is likely to cause inaccuracy of the algorithm. In order to fully consider the temporal and spatial characteristics of the influencing factors of the public cultural service, the ConvLSTM algorithm was adopted in this study. In this study, the data of the relevant characteristics of the public cultural service industry are processed into the form of time series, which will be input into the ConvLSTM network. It determines the label data according to the sliding window and the sliding step size.Figure 3
The ConvLSTM method in the process of multisource data mapping in public cultural service.Neural network methods all use the derivation operation method because the gradient method is a derivation operation process. In the deep learning algorithm framework, the software will automatically realize the automatic differentiation function. Equations (5) and (6) show the derivation process of the weights and biases of each hidden layer.(5)Δωji=−η∂E∂ωji,(6)Δuij=−η∂E∂∂uij.There are many hyperparameters involved in the convolution operation, which are also parameters that need to be continuously adjusted during the training process. Equation (7) shows the calculation rules for hyperparameters.(7)w′=w+2p−ks+1.The ConvLSTM algorithm has the ability to retain historical state information, which is mainly due to the existence of the gate structure. Equation (8) shows the operation rule of the forget gate, which can selectively retain and forget historical information.(8)αlt=∑t=1kωilxit+∑b=1vωctsbt−1.The input gate is the structure of the input data of the ConvLSTM algorithm. It can jointly input the current state data and historical state information to assign different weights. Equations (9) and (10) show the operation rules of the input gate and activation function.(9)it=σWxi∗xt+Whi∗ht−1+Wci∘Ct−1+bi,(10)ft=σWxf∗xt+Whf∗ht−1+Wcf∘Ct−1+bf.Equation (11) shows the operation of the refresh variable.(11)Ct=ft∘Ct−1+it∘ELUWxc∗xt+Whc∗ht−1+bc.Equation (12) shows the operation rule of the output gate, which will output the current state data and historical state data after the nonlinear operation by weight distribution.(12)ot=σWxo∗xt+Who∗ht−1+Wco∘Ct+bo.


## 3.4. Process Flow of Multisource Data in the Public Cultural Service

Both the data fusion method and the neural network method use the weight and bias distribution method to perform the gradient descent operation. This process requires the distribution and magnitude of the data set to be consistent. Otherwise, if the distribution of the data set is uneven, it is easy to cause uneven weight distribution, which may lead to overfitting. The phenomenon of overfitting is that the accuracy of the training set is good, but the performance of the test set will be poor. For the multisource data of the public cultural service, it is even more necessary to preprocess the data of the training set and the test set. The source and form of multisource data will be very different. Excessive data will cause more weight distribution, which will further lead to the model only performing better on features with larger data sets. Therefore, this study adopts the normalization method to preprocess the multisource data sets. The data preprocessing process of this study adopts the normalization method, and the relevant data of the public cultural service industry will be processed into data that conforms to the normal distribution.


## 4. Result Analysis and Discussion

The application of data fusion in the public cultural service is mainly reflected in two processes, the application of the clustering method in the classification process of the influencing factors of the public cultural service and the application of the neural network method in the prediction of the influencing factors. This section mainly discusses the feasibility and accuracy of the data fusion method applied in the public cultural service. In Figure4, the green area represents resident satisfaction. The red areas represent the needs of residents. The blue area represents the economic level of the government. The yellow area represents the policy of the policy. Figure 4 shows the classification results of the four influencing factors of the public cultural service by using the clustering method. It can be clearly seen that the classification effect is relatively good. Figure 4 is to illustrate the accuracy of big data technology in dealing with the public cultural service industry. This schematic diagram can intuitively show the effect of classification. The distances of the four different influencing factors are clearly segmented. For the same influencing factors, their correlations are classified relatively close. From these two points, it can be seen that the clustering method has a better classification effect on the influencing factors in the public cultural service. This good classification effect will be beneficial to the prediction of the public cultural service by the neural network method.Figure 4
The classification results of influencing factors of the public cultural service industry.Figure5 shows the classification errors of four factors affecting the public cultural service. Overall, the clustering method has achieved good results in classifying the four influencing factors of public cultural services. The classification errors of the four influencing factors are all within 3%, which is enough confidence for government managers to complete this task. The largest error is only 2.83%, and this part of the error comes from residents' satisfaction. This part is also the focus of this study because the starting point of the construction of the public cultural service is the satisfaction of residents. The reason for this large error is mainly due to the unevenness of the satisfaction of different residents, but this error is also trustworthy enough for government managers to refer to it. The smallest error is only 1.48%, and this part of the error comes from the government policy factors. This part of the error changes is relatively small, and the government policy factors are relatively stable, so the error is relatively small. Overall, the clustering method is relatively reliable in the classification task of influencing factors of the public cultural service.Figure 5
The classification error of influencing factors of the public cultural service.The prediction accuracy of neural network is the focus of this paper. Figure6 shows the prediction error of residents' demand for the public cultural service. In Figure 6, the linear correlation coefficient can reflect the correlation between the actual value and the predicted value of the public cultural service industry. Generally speaking, if the linear correlation coefficient exceeds 0.95, it means that the predicted value has achieved a good prediction effect. The closer the data points of the linear correlation coefficient are to the linear function y = x, the better the prediction performance of the model. It can be seen from Figure 6 that the data value of the residents' demand is in good agreement with the actual demand value, which is beneficial and convincing enough for the government’s decision-making of the public cultural service. It can be seen from the linear correlation diagram that the data points are distributed on both sides of the linear function, and the linear correlation coefficient basically exceeds 0.965. These data points basically include the distribution of different successful cases. It can be seen that the data points are distributed evenly in different data intervals. From the above description, it can be seen that the neural network method has obvious advantages in predicting the demand of residents for the public cultural service.Figure 6
The prediction results of residents' preference degree through the ConvLSTM method.Residents' satisfaction with the public cultural service is also an important factor for the success of government decision-making because residents can only consume these public cultural service products more actively when they are satisfied with these public service products. Figure7 shows the predicted distribution of residents' satisfaction with the public cultural service. In this study, 40 sets of data were selected as the comparison between the predicted value and the actual resident satisfaction data value. It can be seen from Figure 7 that most of the data values are in good agreement with the actual satisfaction data, and only a few data values have large errors, which is also an acceptable error trend. For the average of all the predicted values of satisfaction, the predicted average of resident satisfaction is in good agreement with the actual average, which can be seen from the green curve in Figure 7.Figure 7
The prediction results of residents demand by the ConvLSTM method.The economic level of the government will determine the type and scale of the public cultural service. The different types of public cultural services in different regions are determined by the economic level. Figure8 demonstrates the feasibility of the neural network approach in predicting the economic level of the government. Similarly, this paper selects 40 sets of data for accuracy verification. It can be seen from Figure 8 that the neural network has high accuracy in predicting the economic level of the government, whether it is the size of the data value of different data points or the changing trend of the data set. Only a few data points are in poor agreement, possibly due to the volatility of the government economy over time.Figure 8
The prediction results of government economic level by network method.Figure9 shows the error distribution of the neural network method in predicting the four influencing factors of the public cultural service. It can be seen that all errors are within 3.11%. The largest forecast error comes from residents satisfaction. The prediction error of residents' demand is only 2.05%, both of which are relatively small, which is beneficial to the government decision-making and management evaluation for the construction of the public cultural service. From Figure 9, it can be seen that the forecast error of the government’s economic level is only 2.34%, and the forecast error of the government policy is 1.54%, which are all acceptable errors.Figure 9
Prediction error of influencing factors of the public cultural service.


## 5. Conclusions

With the continuous development of urbanization and new rural construction and the improvement of the country's comprehensive strength, the public cultural service has developed rapidly. The public cultural service is a convenient government task, which can improve the living standard and life happiness index of residents. The traditional public cultural service is often based on government managers for decision-making and construction, but this method often ignores the needs and satisfaction of residents, which leads to the waste of the public cultural service. This paper uses data fusion and neural network methods to classify and predict the existence of four key factors affecting the public cultural service and analyze some statistical parameters. This research studies the public cultural service by taking residents' demand, residents' satisfaction, government economic level, and government policy as the main four influencing factors. The ConvLSTM algorithm is used to fully mine the temporal and spatial features of the four influencing factors. Clustering method can be well applied in the classification task of the public cultural service. It can effectively classify four different influencing factors. The largest classification error is 2.83%, and this part of the error comes from residents’ satisfaction. The maximum prediction error is 3.11%, which also comes from residents' satisfaction. This shows that residents' satisfaction is a key factor affecting the success of the public cultural service. This error is a sufficient trust error for government managers, which is beneficial to their decision-making and evaluation management of the public cultural service. The classification error for residents' demand is only 1.92%, and this part of the error also meets the government’s work demand. The forecast error for residents demand is only 2.05%. The neural network method has a good linear correlation in predicting residents' satisfaction, and the linear correlation coefficient basically exceeds 0.965, which is a good correlation between the predicted value of residents’ satisfaction and the actual residents' satisfaction. In general, the data fusion method and neural network method have better performance in the classification and prediction tasks of the public cultural service, which provides great convenience for government managers. From the long-term perspective of the government construction of public cultural services, this is also a good model.



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*Source: 3464221-2022-06-15.xml*