# A Deep Learning-Based Model for Analyzing Social Public Issues

Authors: Yue Zhu
Journal: Security and Communication Networks
 (2022)

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## Abstract

Social work services have grown in popularity in China in recent years, and in the context of the government's support of social governance innovation, social work has emerged as a powerful tool for intervening in communal public concerns. Community public problems are a focused expression of urban community contradictions and disputes. The aging and damage problems of public facilities in old building areas without property management in urban communities are affecting the lives of residents, and how social workers should intervene in the increasing public problems in communities can no longer be ignored. This article takes the behavior of social workers as the research object. By summarizing and analyzing the existing research results at home and abroad, it clarifies the importance of social workers' behavior to solve the public problems in communities. In order to analyze the group behavior of social workers, a hierarchical analysis model for group behavior identification is proposed by combining deep neural networks. The method uses moderation network migration learning to achieve the detection of temporal consistency of multiple human bodies in behavioral groups; the recognition of individual behaviors with unconstrained duration in-group behaviors is completed through the fusion of spatiotemporal feature learning; the stable and effective recognition of g is achieved through the fusion of individual behavior categories in scenes and the contextual information of interaction scenes. It is experimentally verified that the method can detect social workers' group behaviors, promote the rational solution to community public problems, and drive the development of community multibody governance.


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## Body

## 1. Introduction

With the development of human society and the improvement in governance, some single, linear, and procedural public management problems have been solved. However, due to the fragmentation of social structure, decentralization of social interests, and diversification of social demands, more and more individualized public issues such as neighborhood living, community life, community health, community safety, and other events have started to emerge [1]. These public problems are characterized by high-value conflict, unstructured and uncertainty, and cannot be properly solved by the government, market, or society alone. The reason is that public problems have their objective social laws, while changes in social governance systems and modes of governance need to go through a long period of practical exploration and experience accumulation.Community is the basic unit of a city, and community construction is the starting point for solving community conflicts and coping with community problems, and promoting community autonomy is an effective way to alleviate community problems and conflicts, and unite the power of atomized residents, and let them participate in community governance to achieve a state of “self-sustainability.” The development of community self-governance can be promoted by bringing together the atomized residents and involving them in community governance to achieve a state of “self-sustainability.” Raising residents' awareness of community participation, stimulating their motivation to participate in the community, and promoting community autonomy are the keys to promoting community governance development and are the foundation for realizing the participation of diverse subjects in social governance. Our government has achieved great successes in social governance with the envisaged creation of the smart community, whose schematic diagram is presented in Figure1, but there are also challenges [2]. To achieve the goal of benign interaction between government governance and social regulation and residents' autonomy, the role of social organizations needs to be brought into play. Specifically, community public problems can be divided into three major categories: community security and order problems, community service problems, community environmental problems, etc. According to the current public problems in the community, community public problems are sorted out, among which basic community public problems refer to the problems of urban community infrastructure construction, such as broken roads, sewer pipes in disrepair, missing street lights in alleys, blocked fecal drains, etc.; security community public problems mainly refer to the problems of poor security in some old-type communities without property management; service community public problems refer to the community public welfare problems based on community service projects and service facilities to provide public services for the disadvantaged groups in the community.Figure 1
Schematic diagram of smart community.The governance of the above problems is even more important to solve from both theoretical and temporal aspects.


### 1.1. Theoretical Significance

Due to the weak blood ties of urban community residents, there are problems such as insufficient awareness and enthusiasm for residents' community participation, and a lack of residents' ability to mobilize and integrate resources inside and outside the community, community public problems are difficult to be solved. Under the guidance of social capital theory, social workers mobilize and integrate resources from streets, neighborhood committees, residents, and enterprises, including actual or potential resources such as human resources and economic resources, to effectively solve problems by nurturing community residents' self-governing organizations, building social relationship networks of community residents, and gathering community residents' strength to form trusting and normative social capital [3]. The study of social workers' intervention in incidents distills the localized theory of social workers' intervention in community public issues, provides theoretical guidance for social workers' intervention in community public issues, and helps enrich the content of social workers' participation in community governance.


### 1.2. Practical Significance

Social work has natural advantages in participating in community construction, but community social work carried out rigidly according to the community planning, community mobilization, and community development models often affect the value of practice-oriented and demand-oriented social work practice. Social workers are an important force participating in social governance in China. Social workers have been providing direct services in the community for many years, but social workers are still in the exploratory stage in promoting community residents' autonomy and intervening in community public issues.Social work has become an important subject of social governance in the West because of its unique function and remarkable effectiveness in dealing with social problems, alleviating social conflicts, and preventing social risks. Since the government introduced social work organizations as third parties to provide social services for community residents, social work has played an important role in helping disadvantaged social members, coordinating social relations, carrying out social services, and promoting social harmony by adhering to the concept of helping people in distress and helping people to help them [4]. Social work has been effective in providing social services for the disadvantaged in the community, but it is still in the exploratory stage of participating in solving community public problems that plague many community residents. It is necessary to continuously practice to refine community social work methods with local characteristics in China, enhance community participation motivation and awareness of community autonomy, strengthen the community governance capacity of community residents, and promote community autonomy. Only by doing so can we better achieve the goal of social governance in the new era. The analysis of social workers' group behavior has become the key to promoting the solution of community problems.In the area of group behavior identification, many researchers have conducted a lot of research and achieved a series of results. There have been a lot of results on group behavior recognition based on traditional machine learning methods, but the recognition effect of these methods is somewhat different from that of deep learning-based methods. In recent years, deep learning-based group behavior recognition methods have gained more and more attention. The majority of studies have focused on identifying and detecting group behavior categories, with relatively little effort done on analyzing and identifying various layers of group behavior. Currently, cascading approaches to group behavior recognition rely on trained human detectors in target detection databases to recognize and track active persons in video frames, with further processing. There is no joint optimization for the detection and tracking of multi-target human bodies in multi-person scenes. In terms of feature characterization, feature extraction is also based on the detected active human body, while ignoring the scene context information and interaction context information [5]. These problems have stimulated the consideration and research of hierarchical group behavior recognition models. Based on the existing work, we rely on the powerful learning capability of deep network architecture to achieve temporal consistency detection of multi-behavior humans by migrating the target detector to the video analysis scenario of group behavior; then, we fuse spatiotemporal feature learning to achieve temporally unconstrained individual behavior recognition. Finally, the group behaviors are effectively recognized by combining the recognized individual behavior categories, captured scene context information, and behavior interaction context information. The model analyzes group behaviors hierarchically and progressively from the semantic level, with each layer being semantically independent of each other and closely related in feature extraction and analysis processing, and each layer ultimately serves to identify group behaviors together. The model has the following three advantages:(1)
Based on migration learning through tunable networks to achieve the detection of multiple human targets, using a small amount of labeled information in the target domain can significantly improve the detection of human bodies in group behavior scenarios and reduce the need for the number of labeled samples in group behavior scenarios.(2)
Regarding the identification of individual behaviors in groups, considering the autonomy of actors and their interaction with other individuals, as well as the suddenness and randomness of their behavioral changes, an individual behavior identification algorithm with unconstrained duration is realized.(3)
The group behavior identification method combining individual behavior types with scene context and interaction context takes into account the important factors necessary for the development of group behavior.At present, the exploration of social workers' participation in community governance is relatively rich, mainly focusing on the macrolevel of community autonomy, relationship with the government, the dilemma of community governance and countermeasures, and the role played by social workers, which provide experience reference for the study of social workers' intervention in community public issues [6]. However, there is a lack of research on social workers' intervention in community public issues at the microlevel, and scholars are still exploring the strategies and dilemmas related to social work intervention in community public issues. In the process of social workers' intervention in community public issues, how to raise residents' awareness of community participation, stimulate residents' participation, link actual or potential resources in the community, and what strategies are applicable to social workers' intervention in community public issues are questions that still need to be thoroughly explored and carefully studied. On the basis of this study, we analyze the behaviors of social workers in the process of intervention in community public issues from the perspective of social workers' behaviors and refine the intervention strategies in the process of intervention, that is what behaviors social workers adopt to stimulate residents' motivation, cultivate residents' organizations, gather residents' strength, mobilize and integrate actual or potential resources in the community, and promote the solution of community public problems.The rest of the article is organized as follows: Section2 discusses the related work. Section 3 discusses the algorithm design of the proposed work, and its subsections discuss the experiments and results. Finally, the conclusion is given in Section 4.


## 1.1. Theoretical Significance

Due to the weak blood ties of urban community residents, there are problems such as insufficient awareness and enthusiasm for residents' community participation, and a lack of residents' ability to mobilize and integrate resources inside and outside the community, community public problems are difficult to be solved. Under the guidance of social capital theory, social workers mobilize and integrate resources from streets, neighborhood committees, residents, and enterprises, including actual or potential resources such as human resources and economic resources, to effectively solve problems by nurturing community residents' self-governing organizations, building social relationship networks of community residents, and gathering community residents' strength to form trusting and normative social capital [3]. The study of social workers' intervention in incidents distills the localized theory of social workers' intervention in community public issues, provides theoretical guidance for social workers' intervention in community public issues, and helps enrich the content of social workers' participation in community governance.


## 1.2. Practical Significance

Social work has natural advantages in participating in community construction, but community social work carried out rigidly according to the community planning, community mobilization, and community development models often affect the value of practice-oriented and demand-oriented social work practice. Social workers are an important force participating in social governance in China. Social workers have been providing direct services in the community for many years, but social workers are still in the exploratory stage in promoting community residents' autonomy and intervening in community public issues.Social work has become an important subject of social governance in the West because of its unique function and remarkable effectiveness in dealing with social problems, alleviating social conflicts, and preventing social risks. Since the government introduced social work organizations as third parties to provide social services for community residents, social work has played an important role in helping disadvantaged social members, coordinating social relations, carrying out social services, and promoting social harmony by adhering to the concept of helping people in distress and helping people to help them [4]. Social work has been effective in providing social services for the disadvantaged in the community, but it is still in the exploratory stage of participating in solving community public problems that plague many community residents. It is necessary to continuously practice to refine community social work methods with local characteristics in China, enhance community participation motivation and awareness of community autonomy, strengthen the community governance capacity of community residents, and promote community autonomy. Only by doing so can we better achieve the goal of social governance in the new era. The analysis of social workers' group behavior has become the key to promoting the solution of community problems.In the area of group behavior identification, many researchers have conducted a lot of research and achieved a series of results. There have been a lot of results on group behavior recognition based on traditional machine learning methods, but the recognition effect of these methods is somewhat different from that of deep learning-based methods. In recent years, deep learning-based group behavior recognition methods have gained more and more attention. The majority of studies have focused on identifying and detecting group behavior categories, with relatively little effort done on analyzing and identifying various layers of group behavior. Currently, cascading approaches to group behavior recognition rely on trained human detectors in target detection databases to recognize and track active persons in video frames, with further processing. There is no joint optimization for the detection and tracking of multi-target human bodies in multi-person scenes. In terms of feature characterization, feature extraction is also based on the detected active human body, while ignoring the scene context information and interaction context information [5]. These problems have stimulated the consideration and research of hierarchical group behavior recognition models. Based on the existing work, we rely on the powerful learning capability of deep network architecture to achieve temporal consistency detection of multi-behavior humans by migrating the target detector to the video analysis scenario of group behavior; then, we fuse spatiotemporal feature learning to achieve temporally unconstrained individual behavior recognition. Finally, the group behaviors are effectively recognized by combining the recognized individual behavior categories, captured scene context information, and behavior interaction context information. The model analyzes group behaviors hierarchically and progressively from the semantic level, with each layer being semantically independent of each other and closely related in feature extraction and analysis processing, and each layer ultimately serves to identify group behaviors together. The model has the following three advantages:(1)
Based on migration learning through tunable networks to achieve the detection of multiple human targets, using a small amount of labeled information in the target domain can significantly improve the detection of human bodies in group behavior scenarios and reduce the need for the number of labeled samples in group behavior scenarios.(2)
Regarding the identification of individual behaviors in groups, considering the autonomy of actors and their interaction with other individuals, as well as the suddenness and randomness of their behavioral changes, an individual behavior identification algorithm with unconstrained duration is realized.(3)
The group behavior identification method combining individual behavior types with scene context and interaction context takes into account the important factors necessary for the development of group behavior.At present, the exploration of social workers' participation in community governance is relatively rich, mainly focusing on the macrolevel of community autonomy, relationship with the government, the dilemma of community governance and countermeasures, and the role played by social workers, which provide experience reference for the study of social workers' intervention in community public issues [6]. However, there is a lack of research on social workers' intervention in community public issues at the microlevel, and scholars are still exploring the strategies and dilemmas related to social work intervention in community public issues. In the process of social workers' intervention in community public issues, how to raise residents' awareness of community participation, stimulate residents' participation, link actual or potential resources in the community, and what strategies are applicable to social workers' intervention in community public issues are questions that still need to be thoroughly explored and carefully studied. On the basis of this study, we analyze the behaviors of social workers in the process of intervention in community public issues from the perspective of social workers' behaviors and refine the intervention strategies in the process of intervention, that is what behaviors social workers adopt to stimulate residents' motivation, cultivate residents' organizations, gather residents' strength, mobilize and integrate actual or potential resources in the community, and promote the solution of community public problems.The rest of the article is organized as follows: Section2 discusses the related work. Section 3 discusses the algorithm design of the proposed work, and its subsections discuss the experiments and results. Finally, the conclusion is given in Section 4.


## 2. Related Works

In this chapter, we discuss the current status of social work intervention and current status of group behavior detection in detail.


### 2.1. Current Status of Social Work Intervention

At present, China's community governance is facing many challenges, and the traditional governance subject has certain limitations. With the deepening reform of social governance, innovative social governance methods are proposed to provide conditions for social workers to participate in community governance and intervene in community public issues. According to relevant studies, the current community governance dilemma is caused by an unclear division of administrative and service functions at the grassroots level, the diminished role of government and neighborhood committees, the administrative single order of community governance, and insufficient community participation; however, the separation of administration and service, the promotion of residents' autonomy, and the cultivation of community spirit are solutions. Regarding the research on the dilemma of social work intervention in community public issues, scholars mainly study it from the context of government purchase of services [7]. Some scholars argue that professional social workers are absorbed into the power network process of the street in the process of service, creating the problems of administrativeization of external services, the bureaucratization of internal governance, and professional establishment. Social work is an important force in community governance and needs to avoid administrative tendencies when intervening in community public issues in order to better play its professional role.As for the research on the process of social workers' intervention in community public issues, some scholars, in studying the case of social workers' intervention in the noise nuisance problem in the square, summarized the steps of emergency intervention, determining the subject of governance, communication, and coordination, and reaching consensus. Some scholars believe that in the process of social workers' intervention in community public problems, they need to adopt a structural orientation to analyze and assess the needs of public problems, focus on good interaction with governmental functionaries, and adopt macroscopic thinking of system coordination. Other scholars believe that social workers need to go through the process of comprehensive research, assessment of public issues, accurate positioning of social workers' intervention roles, and formulation of intervention plans in order to intervene in community public issues [8]. Most of the community work plans in Hong Kong are guided by the “phased intervention approach,” which divides the specific process of community work into clear and feasible stages or steps with specific actions to be implemented. This process is usually divided into a period of exploration, initiation, consolidation, and conclusion. Some scholars believe that social workers should find the environment and institutional factors of the problem, explore the available resources, have good interaction with governmental departments, etc. Some scholars, from the perspective of governance, distill the general process of social work intervention in public problems by studying the case of social work intervention in the noise nuisance in the square. Some scholars also summarized the strategies of social workers' intervention in community public issues, such as home visit strategy, coordination strategy, publicity strategy, and multi-party coordination. Some other scholars say that social workers need to grasp the strategy of “giving priority to emergency,” assess the extent of the problem, and adopt effective methods to solve it.For different community public problems, the role of social workers differs according to different modes of social work intervention. Some scholars believe that social workers are the advocates and coordinators of community work, who mobilize community residents to pay attention to community problems, encourage them to unite and fight for their own rights and interests, enhance their sense of belonging, and promote their cooperation with each other [9]. Some scholars believe that due to the wide range of social work practices, social workers are required to play the professional roles of planners, community organizers, managers, or administrators. Other scholars believe that community social workers play four main roles in intervening in community issues: educator, catalyst, and facilitator and bridge roles. In the domestic aspect of the study, in the document of the Ministry of Civil Affairs, the roles of community social workers mainly include the roles of therapist, educator, facilitator, and organizer. Some scholars in the study of social workers' intervention in the problem of noise nuisance in the square said that social workers take the role of coordinator to build a dialogue platform for various stakeholders, play the role of suggested to put forward feasible intervention suggestions, and play the role of communicator to allow various stakeholders to communicate and negotiate to reach a consensus. Some scholars believe that social workers should take on multiple roles, including important roles such as service providers, supporters, advocates, managers, resource managers, and policy influencers. Some scholars believe that the roles that social workers need to play mainly include the roles of service provider, mediator, community rights defender, administrator, organizer, educator, and researcher. In the body of a community social worker, these roles are frequently crossed and exacerbated. Other academics argue that social workers serve as intermediates, specialists, enablers, planners, and advocates in community work settings [10]. Despite the fact that various researchers have differing viewpoints, scholars have a similar understanding of the duties of social workers into communities. In general, there can be many roles of social workers in community services, and whether they play one role or a mixture of roles requires social workers to choose according to specific community public issues and community situations.


### 2.2. Current Status of Group Behavior Detection

A large number of results exist in the field of multi-target detection. Most of the current research methods rely on sliding window methods, target proposal mechanisms, and convolutional neural networks for target detection and identification. A large number of current target detection methods do not perform joint inference on the presence of the target object but rely on heuristic postprocessing steps to obtain the final multi-target detection results. A notable exception is an algorithm, which is specifically designed to handle target detection problems in multi-target scenarios by training detection models in an end-to-end manner [11]. In addition, there are also generative model-based approaches dedicated to the problem of joint multi-target detection; however, they require multi-view or depth map information and are not applicable to the processing of monocular camera acquisition information. Due to the advantages of migration learning in terms of time efficiency and performance, migration learning has received increasing attention from academia since 1995, and many excellent research results have been achieved. According to the source domain and target domain data distribution, label distribution, feature space, or whether the task is consistent, migration learning can be classified into many categories and the research is very heterogeneous. The research design in this article migrates the target detection problem to the detection of the target human body in a behavior recognition scenario belonging to the direct push transfer learning. In the case that the feature spaces of the source and target domains are basically the same, the methods to realize the direct-push transfer learning is mainly divided into four categories: instance-based, feature-based, parameter-based, and relationship-based transfer learning. The direct generalization of these transfer learning methods does not well solve the problem presented in this article. First, the human labeling information in the behavior recognition scenario involved in this article is relatively small, lacking sufficient labeled samples, and there may also be a very serious positive and negative sample imbalance problem; second, the source domain of this transfer learning belongs to the target detection domain, while the target domain belongs to the behavior recognition domain, and there may be a negative migration problem of samples in some scenarios [12]. To solve the negative migration problem, the network features of negative migration are suppressed by using the modulated network adaptively to select the effective network by weight learning in order to improve the detection effect of human targets in group behavior recognition scenarios.For individual behavior recognition, a large number of traditional hand-designed feature-based methods have emerged, such as HOG, HOF, and MBH [13]. A comprehensive comparison of these behavior recognition methods shows that they first detect regions of interest by various modeling methods, then extract features from the regions of interest, and then use classification models for classification or detection. Or the motion vector and energy obtained by tracking the motion target will be used as the basis for judgment, and the obtained features will be classified by a pretrained classifier to obtain the final analysis results. However, in the experimental process, the analysis is a series of problems, resulting in a large false alarm rate of human detection. In addition, the background of the behavior occurrence scene is usually complex and not easy to extract completely; there are many types of abnormal events themselves, which are not easy to classify; the occlusion situation between people is also serious, which is not easy to distinguish and track, all of which present challenges for effective behavior recognition. Along with the development of deep neural networks, feature expressions extracted by using deep learning methods can effectively compensate for the shortcomings of manual features, which exhibit stronger robustness. Recently, a large number of data-driven methods based on deep learning have emerged for behavior recognition. The most extensively used is a deep convolutional neural network, which uses multilayer convolution and pooling operations to uncover high-level video representations of high-level semantic information and has produced good results in video or behavior categorization. Other implementations of this family of algorithms include independent subspace analysis networks and constrained Boltzmann machines. In addition, recurrent neural network-based behavior recognition methods have gained general attention, and their data processing flow is shown in Figure 2. Most of these methods are dedicated to the behavior recognition of single active individuals and are difficult to be directly applied in behavior recognition scenarios with multiple interactions. In this article, the group behavior is analyzed in a progressive hierarchical manner, and the individual behavior detected and identified is an individual in the group, which is not an independent individual but interacts and depends on other individuals in the group, and the results of individual behavior identification will be used in the subsequent group behavior identification [14]. The proposed individual behavior identification scheme should take into account all aspects and can contribute to the subsequent group behavior identification of the hierarchical analysis model.Figure 2
Recursive neural network data processing flow chart.There are many kinds of methods for group behavior recognition, and according to the modeling model of group behavior, the existing methods for group behavior recognition can be broadly classified into three major categories, namely grammar model-based methods, graph model-based methods, and deep network model-based methods. Currently, most of the research on group behavior recognition is based on graph models and deep network models. Regarding graph model approaches for group behavior recognition, there has been a large amount of work based on hand-designed features to construct graph models. Nowadays, many approaches combine graph models with network architectures to identify group behaviors. Unlike those design feature-based approaches, these methods combine the powerful distinguishability of neural networks with the structure-shaping ability of graph models. The literature [15] proposes an approach to refine the acquisition of individual-level behavioral class estimates from convolutional neural networks (CNNs) through inference, which designs a graph model with trainable nodes representing people and scenes and achieves final scene-level behavioral estimates through information transfer between nodes. Nowadays, the method based on deep neural network models has achieved good results. The literature [16] used long short-term memory (LSTM) networks to identify individual-level and group behaviors for representations, respectively, and pooled their resultant maxima and passed them as inputs to a second LSTM to capture scene-level information representations. The literature [17] utilizes deep reinforcement learning for constructing relationships between low-level features and high-level features for group behavior recognition. The literature [18] utilizes two-dimensional pose networks and three-dimensional CNNs to extract features and construct actor-transformer models to identify individual and group behaviors. A slightly different approach was explored in the literature [19], where the authors noticed that in some cases the class of group behavior is determined by the behavior of an individual, and proposed a soft attention mechanism to identify that individual behavior, and the overall model of this approach is very close to the literature [16]. The literature [20] also uses a deep learning architecture to identify group behaviors by describing local information. The above methods solve the problem of group behavior recognition in some aspects, but some of them use a joint inference method in the inference phase that may discard useful contextual information; some of them are based on the annotated human location information or the tracking results of the human body for subsequent processing and do not achieve an end-to-end solution for the analysis and recognition of group behavior.


## 2.1. Current Status of Social Work Intervention

At present, China's community governance is facing many challenges, and the traditional governance subject has certain limitations. With the deepening reform of social governance, innovative social governance methods are proposed to provide conditions for social workers to participate in community governance and intervene in community public issues. According to relevant studies, the current community governance dilemma is caused by an unclear division of administrative and service functions at the grassroots level, the diminished role of government and neighborhood committees, the administrative single order of community governance, and insufficient community participation; however, the separation of administration and service, the promotion of residents' autonomy, and the cultivation of community spirit are solutions. Regarding the research on the dilemma of social work intervention in community public issues, scholars mainly study it from the context of government purchase of services [7]. Some scholars argue that professional social workers are absorbed into the power network process of the street in the process of service, creating the problems of administrativeization of external services, the bureaucratization of internal governance, and professional establishment. Social work is an important force in community governance and needs to avoid administrative tendencies when intervening in community public issues in order to better play its professional role.As for the research on the process of social workers' intervention in community public issues, some scholars, in studying the case of social workers' intervention in the noise nuisance problem in the square, summarized the steps of emergency intervention, determining the subject of governance, communication, and coordination, and reaching consensus. Some scholars believe that in the process of social workers' intervention in community public problems, they need to adopt a structural orientation to analyze and assess the needs of public problems, focus on good interaction with governmental functionaries, and adopt macroscopic thinking of system coordination. Other scholars believe that social workers need to go through the process of comprehensive research, assessment of public issues, accurate positioning of social workers' intervention roles, and formulation of intervention plans in order to intervene in community public issues [8]. Most of the community work plans in Hong Kong are guided by the “phased intervention approach,” which divides the specific process of community work into clear and feasible stages or steps with specific actions to be implemented. This process is usually divided into a period of exploration, initiation, consolidation, and conclusion. Some scholars believe that social workers should find the environment and institutional factors of the problem, explore the available resources, have good interaction with governmental departments, etc. Some scholars, from the perspective of governance, distill the general process of social work intervention in public problems by studying the case of social work intervention in the noise nuisance in the square. Some scholars also summarized the strategies of social workers' intervention in community public issues, such as home visit strategy, coordination strategy, publicity strategy, and multi-party coordination. Some other scholars say that social workers need to grasp the strategy of “giving priority to emergency,” assess the extent of the problem, and adopt effective methods to solve it.For different community public problems, the role of social workers differs according to different modes of social work intervention. Some scholars believe that social workers are the advocates and coordinators of community work, who mobilize community residents to pay attention to community problems, encourage them to unite and fight for their own rights and interests, enhance their sense of belonging, and promote their cooperation with each other [9]. Some scholars believe that due to the wide range of social work practices, social workers are required to play the professional roles of planners, community organizers, managers, or administrators. Other scholars believe that community social workers play four main roles in intervening in community issues: educator, catalyst, and facilitator and bridge roles. In the domestic aspect of the study, in the document of the Ministry of Civil Affairs, the roles of community social workers mainly include the roles of therapist, educator, facilitator, and organizer. Some scholars in the study of social workers' intervention in the problem of noise nuisance in the square said that social workers take the role of coordinator to build a dialogue platform for various stakeholders, play the role of suggested to put forward feasible intervention suggestions, and play the role of communicator to allow various stakeholders to communicate and negotiate to reach a consensus. Some scholars believe that social workers should take on multiple roles, including important roles such as service providers, supporters, advocates, managers, resource managers, and policy influencers. Some scholars believe that the roles that social workers need to play mainly include the roles of service provider, mediator, community rights defender, administrator, organizer, educator, and researcher. In the body of a community social worker, these roles are frequently crossed and exacerbated. Other academics argue that social workers serve as intermediates, specialists, enablers, planners, and advocates in community work settings [10]. Despite the fact that various researchers have differing viewpoints, scholars have a similar understanding of the duties of social workers into communities. In general, there can be many roles of social workers in community services, and whether they play one role or a mixture of roles requires social workers to choose according to specific community public issues and community situations.


## 2.2. Current Status of Group Behavior Detection

A large number of results exist in the field of multi-target detection. Most of the current research methods rely on sliding window methods, target proposal mechanisms, and convolutional neural networks for target detection and identification. A large number of current target detection methods do not perform joint inference on the presence of the target object but rely on heuristic postprocessing steps to obtain the final multi-target detection results. A notable exception is an algorithm, which is specifically designed to handle target detection problems in multi-target scenarios by training detection models in an end-to-end manner [11]. In addition, there are also generative model-based approaches dedicated to the problem of joint multi-target detection; however, they require multi-view or depth map information and are not applicable to the processing of monocular camera acquisition information. Due to the advantages of migration learning in terms of time efficiency and performance, migration learning has received increasing attention from academia since 1995, and many excellent research results have been achieved. According to the source domain and target domain data distribution, label distribution, feature space, or whether the task is consistent, migration learning can be classified into many categories and the research is very heterogeneous. The research design in this article migrates the target detection problem to the detection of the target human body in a behavior recognition scenario belonging to the direct push transfer learning. In the case that the feature spaces of the source and target domains are basically the same, the methods to realize the direct-push transfer learning is mainly divided into four categories: instance-based, feature-based, parameter-based, and relationship-based transfer learning. The direct generalization of these transfer learning methods does not well solve the problem presented in this article. First, the human labeling information in the behavior recognition scenario involved in this article is relatively small, lacking sufficient labeled samples, and there may also be a very serious positive and negative sample imbalance problem; second, the source domain of this transfer learning belongs to the target detection domain, while the target domain belongs to the behavior recognition domain, and there may be a negative migration problem of samples in some scenarios [12]. To solve the negative migration problem, the network features of negative migration are suppressed by using the modulated network adaptively to select the effective network by weight learning in order to improve the detection effect of human targets in group behavior recognition scenarios.For individual behavior recognition, a large number of traditional hand-designed feature-based methods have emerged, such as HOG, HOF, and MBH [13]. A comprehensive comparison of these behavior recognition methods shows that they first detect regions of interest by various modeling methods, then extract features from the regions of interest, and then use classification models for classification or detection. Or the motion vector and energy obtained by tracking the motion target will be used as the basis for judgment, and the obtained features will be classified by a pretrained classifier to obtain the final analysis results. However, in the experimental process, the analysis is a series of problems, resulting in a large false alarm rate of human detection. In addition, the background of the behavior occurrence scene is usually complex and not easy to extract completely; there are many types of abnormal events themselves, which are not easy to classify; the occlusion situation between people is also serious, which is not easy to distinguish and track, all of which present challenges for effective behavior recognition. Along with the development of deep neural networks, feature expressions extracted by using deep learning methods can effectively compensate for the shortcomings of manual features, which exhibit stronger robustness. Recently, a large number of data-driven methods based on deep learning have emerged for behavior recognition. The most extensively used is a deep convolutional neural network, which uses multilayer convolution and pooling operations to uncover high-level video representations of high-level semantic information and has produced good results in video or behavior categorization. Other implementations of this family of algorithms include independent subspace analysis networks and constrained Boltzmann machines. In addition, recurrent neural network-based behavior recognition methods have gained general attention, and their data processing flow is shown in Figure 2. Most of these methods are dedicated to the behavior recognition of single active individuals and are difficult to be directly applied in behavior recognition scenarios with multiple interactions. In this article, the group behavior is analyzed in a progressive hierarchical manner, and the individual behavior detected and identified is an individual in the group, which is not an independent individual but interacts and depends on other individuals in the group, and the results of individual behavior identification will be used in the subsequent group behavior identification [14]. The proposed individual behavior identification scheme should take into account all aspects and can contribute to the subsequent group behavior identification of the hierarchical analysis model.Figure 2
Recursive neural network data processing flow chart.There are many kinds of methods for group behavior recognition, and according to the modeling model of group behavior, the existing methods for group behavior recognition can be broadly classified into three major categories, namely grammar model-based methods, graph model-based methods, and deep network model-based methods. Currently, most of the research on group behavior recognition is based on graph models and deep network models. Regarding graph model approaches for group behavior recognition, there has been a large amount of work based on hand-designed features to construct graph models. Nowadays, many approaches combine graph models with network architectures to identify group behaviors. Unlike those design feature-based approaches, these methods combine the powerful distinguishability of neural networks with the structure-shaping ability of graph models. The literature [15] proposes an approach to refine the acquisition of individual-level behavioral class estimates from convolutional neural networks (CNNs) through inference, which designs a graph model with trainable nodes representing people and scenes and achieves final scene-level behavioral estimates through information transfer between nodes. Nowadays, the method based on deep neural network models has achieved good results. The literature [16] used long short-term memory (LSTM) networks to identify individual-level and group behaviors for representations, respectively, and pooled their resultant maxima and passed them as inputs to a second LSTM to capture scene-level information representations. The literature [17] utilizes deep reinforcement learning for constructing relationships between low-level features and high-level features for group behavior recognition. The literature [18] utilizes two-dimensional pose networks and three-dimensional CNNs to extract features and construct actor-transformer models to identify individual and group behaviors. A slightly different approach was explored in the literature [19], where the authors noticed that in some cases the class of group behavior is determined by the behavior of an individual, and proposed a soft attention mechanism to identify that individual behavior, and the overall model of this approach is very close to the literature [16]. The literature [20] also uses a deep learning architecture to identify group behaviors by describing local information. The above methods solve the problem of group behavior recognition in some aspects, but some of them use a joint inference method in the inference phase that may discard useful contextual information; some of them are based on the annotated human location information or the tracking results of the human body for subsequent processing and do not achieve an end-to-end solution for the analysis and recognition of group behavior.


## 3. Algorithm Design

In this section, we define the understanding community needs, social worker behavior recognition, and experimental validation and applications in detail.


### 3.1. Understanding Community Needs

The preliminary work of social workers in intervening in community public issues is mainly to conduct a survey of residents' needs and to explore the social capital existing in the community. Only by conducting a sufficient survey of residents' needs and integrating the social capital existing in the community can social workers assess residents' needs more accurately and lay the foundation for formulating intervention plans. In order to carry out the research work effectively, the social worker formulated the objectives of the research work, through questionnaires, to understand the residents' concerns about the incident and the residents' motivation to participate. Through visiting the affected residents, the social worker assessed the residents' degree of influence and understood their needs of the affected residents. At the same time, the social worker visited the neighborhood committee, visited on-site fields, and asked experts to get comprehensive information about the event and assess the community social capital that exists and can be activated. This questionnaire survey's respondents were long-term residents of a neighborhood, and the questionnaire survey was performed via incidental sampling. One of the methods used by social workers to undertake community research is the questionnaire survey. The neighborhood had a public concern at the time, and it had a wide impact because it was not addressed in a timely manner.The survey data are shown in Table1, only very few residents are more concerned about public issues, and they are continuously concerned about the relevant events and the ins and outs of the district, while those who are completely ignorant of public issues account for more than 60%, thus it can be seen that those who are not very concerned about public issues reach 90%, and the residents of this district are not very aware of public issues, which is related to the seriousness of the residents affected.Table 1
Level of knowledge of public issues in residential communities.
Level of understandingPercentageNo understanding at all62.58General understanding27.09Understanding05.36Very well understood04.97Next, we conducted a survey on residents' willingness to participate in problem-solving, and the survey results are compiled in Table2. 66% of the residents were found to be unwilling to participate in solving the incident, which is more than half of the surveyed population, thus showing that the residents of the neighborhood lacked a sense of identity and belonging to the community, and the residents' motivation, initiative, and enthusiasm for community participation were not high. The reason for this is that the community lacks a platform for residents to communicate and interact with each other, the network of residents is loose, and residents are not familiar with each other, that is, there is a lack of community trust, interaction, norms, and relational network capital.Table 2
Residents' willingness to participate in problem-solving.
Willingness to participatePercentageDon't want to participate66.32Can participate18.54Calling the neighborhood08.61Provide relevant resources09.75Provide advice03.16Communicating with government02.23Finally, we made a survey on the residents' concern for public issues in the community, and the survey data are shown in Table3. From Table 3, we can see that although most residents are not willing to get personally involved, more of them choose to pay attention to the progress of the incident and expect the incident to be solved as soon as possible, and only a very small number of residents stay out of it. Social workers need to innovate residents' interaction mechanisms, build a platform for residents to communicate with each other, improve residents' identification with the community of community life, establish and activate residents' relationship network, stimulate residents' willingness to participate, and enhance their motivation for community participation.Table 3
Residents' concern for public issues in the community.
Level of concernPercentageNo attention at all01.63Some attention07.38Uncertainty about the attention20.81Occasional attention26.42Continuous attention43.76During the field visits, social workers consulted some of the residents and gained a certain understanding of the basic situation of the residents in the district. Most of the residents tend to look for the intervention of external forces, but stay out of the matter themselves, ignoring their own potential power. There are mainly these kinds of opinions: some residents said that the aging buildings in the district are prone to frequent problems, and they think that the street should give them support and fund the maintenance; some other residents think that it is difficult to reach a consensus when everyone's interests are involved, and it is difficult for the residents to solve the problem by themselves, and the neighborhood committee can solve the problem by coming forward to unify their opinions. In this process, the social worker found that the relationship between the residents of the neighborhood was rather rusty, and the trust, interaction, norms, and relational network capital of the community residents were lacking. Although most of the residents were unaware of the public event, the event involved issues of interest to the residents and had different levels of impact on their lives. Even if most residents are not willing to come forward to organize or participate in solving the septic tank blockage problem, they will still continue to follow the progress of the incident and hope that the incident will be solved as soon as possible to restore a good community environment.


### 3.2. Social Worker Behavior Recognition

#### 3.2.1. Multiple Human Target Detection

To address the problem of detecting the temporal consistency of multiple active human bodies in the behavior recognition scene, this article designs a network framework for a temporal consistency detection model of multiple human targets as shown in Figure3. The method first trains the human detector through the ImageNet source data domain, then migrates it to the behavior recognition scene using a dynamic modulation network to densely detect multiple active humans in the behavior video frames; then rejects those duplicate detection results by nonmaximal suppression and optimizes the detection border of active humans Bt; finally, the discriminative model conditional random field by probabilistic inference is used to match the detection results of the same active human in any two consecutive frames to achieve the temporal consistency detection of multiple human targets in the whole video sequence.Figure 3
Migration learning-based temporal consistency detection of multiple human targets.In the person detection phase as shown in Figure3, the model migrates the human target detectors from the target detection domain to the behavior recognition scenario. With sufficient samples in the source domain, assuming that the source domain-human target detection network can adapt to all situations, only some of the networks will work for a given behavior recognition scenario, and some of them are redundant and some of them even bring negative migration [21]. Without keeping the source training samples, the modulated network of this model enhances the recognition effect by the feature-adjusted selection, based on the constructed network model, using a small amount of target domain sample label information to select the effective network adaptively through the learning of weights, and suppressing the network features that cause negative migration. The migration of human detection in the target detection domain to the behavior recognition scenario is achieved by the feature maps’ weight layer, that is, the dynamic regulation of feature selection is achieved by adding a network layer that weights the feature maps, and the parameters of the feature weight layer p=p1,p2,…,pm are learned by the feedback network prediction of the following regulation network. In the training process of the human detection network, firstly, the deep convolutional network based on AlexNet in this article uses the source domain samples to train the network weights, solidify the feature extraction network, and select the feature map output from the intermediate layer; then use RPN (region proposal network) to predict the region where the human target is located, assuming that m feature map of size v1×v2 is obtained, by minimizing the equation (1) objective function to train.(1)L=12N∑i=1Nziw,p−yi2+αp1.The two network parametersω and p are trained by cross-iterative implementation, and only the feature map weighting layer and the fully connected layer are trained, which can be implemented by the standard error propagation algorithm; the training of the region prediction neural network is detailed in the Faster R-CNN; the training method of the moderation network is detailed in the adaptive human detection algorithm [22]. In the human detection stage, the candidate region is obtained by the region prediction network, and then the region modulation network feature weights are used to generate a new target detector to detect the human body in the behavior recognition scene.


#### 3.2.2. Human Behavior Recognition

Individual behavior in group activities is subordinate to a part of the group behavior; in general, behavior individual will interact with other individuals or scenes in the behavior group. In addition, individual behavior is highly subjective and arbitrary, and the duration of similar behavioral activities and the magnitude of actions performed by different actors or the same actor at different moments vary greatly. Taking many factors into account, this article characterizes behaviors through the organic combination of spatial convolutional neural network and motor convolutional neural network and adopts long short-term memory neural networks to realize individual behavior recognition with no constraints on duration [23]. Due to the strong suddenness of individual behavior in group behavior and the subjective arbitrariness of behavior duration, a memory network based on long short-term memory neural networks is used to train the model of the uncertain length of input to the effective output of definite dimension, and to complete the task of effectively outputting the activity state of human behavior at any given time. As shown in Figure 4, the individual behavior recognition method with unconstrained learning duration of fused spatiotemporal features is used for behavior recognition of a behavioral individual in a group behavior scenario.Figure 4
Fusion of spatiotemporal features for learning duration unconstrained individual behavior recognition.The network model uses two types of deep convolutional neural networks to extract feature sequences of behaviors, and after extraction, the spliced feature sequences are fed into the LSTM network for memory learning. The state of individual behaviors in group behavior sometimes does not change for a long period of time, and sometimes changes frequently due to interactions with other active subjects or activity scenarios. Long short-term memory neural networks can effectively remember long or short-term information depending on the input due to the setting of forgetting gates in its network structure, and this feature of it makes it very suitable for problems faced with an uncertain duration of behavior [24]. With the long short-term memory neural network, behavioral states can be recognized at any moment. The activation function of each neuron when recognizing and training the LSTM for individual behaviors is as follows:(2)it=σWxixt+Whiht−1+bi,(3)ft=σWxfxt+Whfht−1+bf,(4)ot=σWxoxt+Whoht−1+bo,(5)gt=tanhWxcxt+Whcht−1+bc,(6)ct=ft⊙ct−1+it⊙gt,(7)ht=ot⊙tanhct.The duration of activities of individuals behaving in group behavior varies greatly. In addition, the occurrence of individual behavioral transitions is often naturally continuous and smooth, and to effectively identify behaviors, the current behavioral state must be identified within a short period of time when the behavioral transitions occur; therefore, behavioral sequences of arbitrary length need to be processed and a valid state output derived. Through testing, it is found that this unconstrained discriminative model with unconstrained input length cannot give correct judgments in time when it is insufficient to determine the current behavioral state because of the shortage of information when the input sequence is too short or the behavioral state of an individual is changed. To avoid misjudgments made by this model, in this case, the proposed memory network model does not discriminate the behavioral state when a definite determination cannot be made. The identification of individual behaviors will have some influence on the behavior of the group, but the absence of identification results for individual behaviors in the behavioral group in the short term will have little effect on them. In addition, as mentioned earlier, group behavior is influenced by many factors, and this scheme not only does not affect the final determination of group behavior but also largely reduces the serious consequences of misclassification.


#### 3.2.3. Group Behavior Identification

The state of group behavior is influenced by many factors, such as the state of individual behavior in the group, the interaction between behavioral individuals, the interaction between the human body and objects, the environment, and the activity scene in which it is located [25]. In order to effectively integrate all kinds of information, this article designs a group behavior recognition process as shown in Figure 5, where the black arrows indicate the first two levels of human consistency detection and individual behavior recognition process. In order to improve the reuse rate of the feature extraction network module in the model, the spatial convolutional neural network and the motor convolutional neural network in the individual behavior recognition module are used to encode the scene context information and behavior interaction context information of group behavior and combine the recognition results of the behavior state of each actor in the group to analyze group behaviors.Figure 5
Group behavior recognition combining individual behavior and interaction scene context.From the group behavior recognition process shown in Figure5, it can be seen that the features used for group behavior recognition mainly contain three parts: the voting features of the recognition results of each individual behavior ftR, the scene context information ftSC, and the interaction context information ftIC. The interaction context information includes the interaction between each active individual in the behavioral group and the interaction between the active individual and the environment, which mainly involves the interaction motion information. We mainly use the AlexNet-based spatial convolutional neural network (SCNN) to encode the scene context information and the GoogLeNet-based motion convolutional neural network (MCNN) to encode the interaction context information in the individual behavior recognition module. The dimensions of the encoding fSC of the scene context information and the encoding fIC of the interaction context extracted using SCNN and MCNN are fixed, and the number of individual behaviors is uncertain due to the difference in the number of actors in each group behavior instance, so it becomes infeasible to directly use the recognition results of each individual behavior as part of the features for group behavior recognition. To solve this problem, this article adopts the unique thermal coding to encode the recognition results of individual behaviors and uses the voting information of each individual behavior category as the state coding of individual behaviors in group behavior recognition fR. Finally, based on these three features fR,fSC,fIC, the long short-term memory neural network is trained using equation (7) for group behavior recognition.


### 3.3. Experimental Validation and Applications

#### 3.3.1. MNIST Classification Recognition Experiments

The experiment is divided into two phases. In the training phase, the MNIST handwritten digit dataset is used for testing; firstly, the initial data input is encoded and then the encoding layer changes the floating point number into a continuous pulse time series of 8 time steps, then it is convolved with 16, 32, 32, and 16 convolution kernels of all sizes 3 × 3 in turn, and finally it goes through a full linkage layer until the output.Figures6 and 7 depict the loss during the training process, as well as the accuracy variation curves tested on the validation set. Figure 6 shows the number of network training iterations on the horizontal axis and the loss on the vertical axis. The loss diminishes dramatically in the early stages of training, from 2.5 to 0.5, as shown by the graphs. After the 50th iteration, the training loss starts to decrease slowly, and after reaching the 200th iteration, the training loss no longer decreases significantly and converges to about 0.2.Figure 6
Neural network training loss curves.Figure 7
Neural network training accuracy curves.The horizontal coordinate of the graph in Figure7 is the epoch and the vertical coordinate is the accuracy tested on the validation set. After the first epoch, the accuracy has reached 65%. After the next 5 epochs, the accuracy has reached 90%, and finally after 15 epochs, the accuracy on the validation set has reached 98%.


#### 3.3.2. Experiments on Volleyball Behavior Detection

In this article, the proposed hierarchical row analysis model is validated on the public dataset-volleyball dataset. In order to verify that all levels of analysis involved in the proposed hierarchical analysis model are effective for group behavior identification, experiments are conducted in this article to compare the proposed method with the set benchmark method on the volleyball dataset. Table4 shows the comparison of the recognition rate of each group behavior using the proposed method and the benchmark method on the volleyball video dataset. This experiment shows that the correct detection of human regions has an important impact on the recognition of individual behaviors in a group.Table 4
Comparison of the experimental results of the proposed method and the benchmark method on volleyball dataset.
MethodlpassrpasslsetrsetSM-CNN0.780.850.840.87Person-LSTM0.820.810.850.82Our0.870.890.880.93Table5 shows the comparison of the average recognition rates of group and individual behaviors in the volleyball video dataset using the proposed method and other classical methods. Currently, most of the recognition algorithms for group behaviors only recognize the whole group activity scene and do not analyze the individual behaviors in the group behaviors. In Table 4, the comparison with the benchmark experiments shows that considering the category information of each individual behavior that constitutes the group behavior when recognizing the group behavior can effectively improve the recognition rate of the group behavior. In Table 5, the recognition of individual behaviors by other group-as-recognition methods that analyze individual behavior recognition is shown. The comparison shows that the proposed algorithm has good effect on the recognition of individual behaviors in groups.Table 5
Comparison of group behavior and individual behavior on the volleyball dataset comparison of the average recognition rate of individual behaviors.
MethodCollectiveIndividualSM-CNN0.810.77Person-LSTM0.790.82Our0.890.84


#### 3.3.3. Practical Application

Our strategy was integrated to examine the impact of social workers' actions on community public issues. We invited social workers to conduct three independent activities: patrolling, garbage collection, and neighborhood exchanges, followed by random community questionnaires. Table6 shows the findings of the questionnaires. It can be seen that simply patrolling has little effect on residents' participation in community issues; trash collection helps to raise residents' awareness of participation, but their concern for public issues remains low; and only when neighbors are acquainted with one another will people actively communicate to solve community public issues. Therefore, during the period of cultivating community social capital, social workers rebuild community trust, promote residents' interaction, and establish the process of community residents' relationship network by cultivating residents' autonomous organizations, communicating and coordinating with other community subjects in order to integrate and activate community social capital. Under the framework of social capital theory, the strategies of social workers' intervention in community public issues are summarized and refined, which mainly include: enhancing residents' motivation for community participation through rebuilding community trust; developing community relationship networks to activate the growth space of residents' motivation for participation; enhancing residents' awareness of community participation in the optimization of community interaction process; building information and resource sharing platform to develop and maintain community social capital; and establishing community. The goal of social workers' intervention in community public events is to solve public problems, while the process goal is to build and improve the trust, interaction, norms, and relational network capital of community residents. Through the three major working techniques of social workers, the social workers innovate residents' interaction mechanisms, build a platform for residents' communication and cooperation, cultivate residents' organizations, and mobilize community social capital in order to increase residents' awareness of community autonomy, strengthen their motivation for community participation, and achieve the effect of intervention goals.Table 6
Effect of social worker's behavior on residents' attention.
Social work behaviorContinuous attention (%)Occasional attention (%)Patrols46.1825.36Trash removal55.3732.54Neighborhood exchange71.4350.86


## 3.1. Understanding Community Needs

The preliminary work of social workers in intervening in community public issues is mainly to conduct a survey of residents' needs and to explore the social capital existing in the community. Only by conducting a sufficient survey of residents' needs and integrating the social capital existing in the community can social workers assess residents' needs more accurately and lay the foundation for formulating intervention plans. In order to carry out the research work effectively, the social worker formulated the objectives of the research work, through questionnaires, to understand the residents' concerns about the incident and the residents' motivation to participate. Through visiting the affected residents, the social worker assessed the residents' degree of influence and understood their needs of the affected residents. At the same time, the social worker visited the neighborhood committee, visited on-site fields, and asked experts to get comprehensive information about the event and assess the community social capital that exists and can be activated. This questionnaire survey's respondents were long-term residents of a neighborhood, and the questionnaire survey was performed via incidental sampling. One of the methods used by social workers to undertake community research is the questionnaire survey. The neighborhood had a public concern at the time, and it had a wide impact because it was not addressed in a timely manner.The survey data are shown in Table1, only very few residents are more concerned about public issues, and they are continuously concerned about the relevant events and the ins and outs of the district, while those who are completely ignorant of public issues account for more than 60%, thus it can be seen that those who are not very concerned about public issues reach 90%, and the residents of this district are not very aware of public issues, which is related to the seriousness of the residents affected.Table 1
Level of knowledge of public issues in residential communities.
Level of understandingPercentageNo understanding at all62.58General understanding27.09Understanding05.36Very well understood04.97Next, we conducted a survey on residents' willingness to participate in problem-solving, and the survey results are compiled in Table2. 66% of the residents were found to be unwilling to participate in solving the incident, which is more than half of the surveyed population, thus showing that the residents of the neighborhood lacked a sense of identity and belonging to the community, and the residents' motivation, initiative, and enthusiasm for community participation were not high. The reason for this is that the community lacks a platform for residents to communicate and interact with each other, the network of residents is loose, and residents are not familiar with each other, that is, there is a lack of community trust, interaction, norms, and relational network capital.Table 2
Residents' willingness to participate in problem-solving.
Willingness to participatePercentageDon't want to participate66.32Can participate18.54Calling the neighborhood08.61Provide relevant resources09.75Provide advice03.16Communicating with government02.23Finally, we made a survey on the residents' concern for public issues in the community, and the survey data are shown in Table3. From Table 3, we can see that although most residents are not willing to get personally involved, more of them choose to pay attention to the progress of the incident and expect the incident to be solved as soon as possible, and only a very small number of residents stay out of it. Social workers need to innovate residents' interaction mechanisms, build a platform for residents to communicate with each other, improve residents' identification with the community of community life, establish and activate residents' relationship network, stimulate residents' willingness to participate, and enhance their motivation for community participation.Table 3
Residents' concern for public issues in the community.
Level of concernPercentageNo attention at all01.63Some attention07.38Uncertainty about the attention20.81Occasional attention26.42Continuous attention43.76During the field visits, social workers consulted some of the residents and gained a certain understanding of the basic situation of the residents in the district. Most of the residents tend to look for the intervention of external forces, but stay out of the matter themselves, ignoring their own potential power. There are mainly these kinds of opinions: some residents said that the aging buildings in the district are prone to frequent problems, and they think that the street should give them support and fund the maintenance; some other residents think that it is difficult to reach a consensus when everyone's interests are involved, and it is difficult for the residents to solve the problem by themselves, and the neighborhood committee can solve the problem by coming forward to unify their opinions. In this process, the social worker found that the relationship between the residents of the neighborhood was rather rusty, and the trust, interaction, norms, and relational network capital of the community residents were lacking. Although most of the residents were unaware of the public event, the event involved issues of interest to the residents and had different levels of impact on their lives. Even if most residents are not willing to come forward to organize or participate in solving the septic tank blockage problem, they will still continue to follow the progress of the incident and hope that the incident will be solved as soon as possible to restore a good community environment.


## 3.2. Social Worker Behavior Recognition

### 3.2.1. Multiple Human Target Detection

To address the problem of detecting the temporal consistency of multiple active human bodies in the behavior recognition scene, this article designs a network framework for a temporal consistency detection model of multiple human targets as shown in Figure3. The method first trains the human detector through the ImageNet source data domain, then migrates it to the behavior recognition scene using a dynamic modulation network to densely detect multiple active humans in the behavior video frames; then rejects those duplicate detection results by nonmaximal suppression and optimizes the detection border of active humans Bt; finally, the discriminative model conditional random field by probabilistic inference is used to match the detection results of the same active human in any two consecutive frames to achieve the temporal consistency detection of multiple human targets in the whole video sequence.Figure 3
Migration learning-based temporal consistency detection of multiple human targets.In the person detection phase as shown in Figure3, the model migrates the human target detectors from the target detection domain to the behavior recognition scenario. With sufficient samples in the source domain, assuming that the source domain-human target detection network can adapt to all situations, only some of the networks will work for a given behavior recognition scenario, and some of them are redundant and some of them even bring negative migration [21]. Without keeping the source training samples, the modulated network of this model enhances the recognition effect by the feature-adjusted selection, based on the constructed network model, using a small amount of target domain sample label information to select the effective network adaptively through the learning of weights, and suppressing the network features that cause negative migration. The migration of human detection in the target detection domain to the behavior recognition scenario is achieved by the feature maps’ weight layer, that is, the dynamic regulation of feature selection is achieved by adding a network layer that weights the feature maps, and the parameters of the feature weight layer p=p1,p2,…,pm are learned by the feedback network prediction of the following regulation network. In the training process of the human detection network, firstly, the deep convolutional network based on AlexNet in this article uses the source domain samples to train the network weights, solidify the feature extraction network, and select the feature map output from the intermediate layer; then use RPN (region proposal network) to predict the region where the human target is located, assuming that m feature map of size v1×v2 is obtained, by minimizing the equation (1) objective function to train.(1)L=12N∑i=1Nziw,p−yi2+αp1.The two network parametersω and p are trained by cross-iterative implementation, and only the feature map weighting layer and the fully connected layer are trained, which can be implemented by the standard error propagation algorithm; the training of the region prediction neural network is detailed in the Faster R-CNN; the training method of the moderation network is detailed in the adaptive human detection algorithm [22]. In the human detection stage, the candidate region is obtained by the region prediction network, and then the region modulation network feature weights are used to generate a new target detector to detect the human body in the behavior recognition scene.


### 3.2.2. Human Behavior Recognition

Individual behavior in group activities is subordinate to a part of the group behavior; in general, behavior individual will interact with other individuals or scenes in the behavior group. In addition, individual behavior is highly subjective and arbitrary, and the duration of similar behavioral activities and the magnitude of actions performed by different actors or the same actor at different moments vary greatly. Taking many factors into account, this article characterizes behaviors through the organic combination of spatial convolutional neural network and motor convolutional neural network and adopts long short-term memory neural networks to realize individual behavior recognition with no constraints on duration [23]. Due to the strong suddenness of individual behavior in group behavior and the subjective arbitrariness of behavior duration, a memory network based on long short-term memory neural networks is used to train the model of the uncertain length of input to the effective output of definite dimension, and to complete the task of effectively outputting the activity state of human behavior at any given time. As shown in Figure 4, the individual behavior recognition method with unconstrained learning duration of fused spatiotemporal features is used for behavior recognition of a behavioral individual in a group behavior scenario.Figure 4
Fusion of spatiotemporal features for learning duration unconstrained individual behavior recognition.The network model uses two types of deep convolutional neural networks to extract feature sequences of behaviors, and after extraction, the spliced feature sequences are fed into the LSTM network for memory learning. The state of individual behaviors in group behavior sometimes does not change for a long period of time, and sometimes changes frequently due to interactions with other active subjects or activity scenarios. Long short-term memory neural networks can effectively remember long or short-term information depending on the input due to the setting of forgetting gates in its network structure, and this feature of it makes it very suitable for problems faced with an uncertain duration of behavior [24]. With the long short-term memory neural network, behavioral states can be recognized at any moment. The activation function of each neuron when recognizing and training the LSTM for individual behaviors is as follows:(2)it=σWxixt+Whiht−1+bi,(3)ft=σWxfxt+Whfht−1+bf,(4)ot=σWxoxt+Whoht−1+bo,(5)gt=tanhWxcxt+Whcht−1+bc,(6)ct=ft⊙ct−1+it⊙gt,(7)ht=ot⊙tanhct.The duration of activities of individuals behaving in group behavior varies greatly. In addition, the occurrence of individual behavioral transitions is often naturally continuous and smooth, and to effectively identify behaviors, the current behavioral state must be identified within a short period of time when the behavioral transitions occur; therefore, behavioral sequences of arbitrary length need to be processed and a valid state output derived. Through testing, it is found that this unconstrained discriminative model with unconstrained input length cannot give correct judgments in time when it is insufficient to determine the current behavioral state because of the shortage of information when the input sequence is too short or the behavioral state of an individual is changed. To avoid misjudgments made by this model, in this case, the proposed memory network model does not discriminate the behavioral state when a definite determination cannot be made. The identification of individual behaviors will have some influence on the behavior of the group, but the absence of identification results for individual behaviors in the behavioral group in the short term will have little effect on them. In addition, as mentioned earlier, group behavior is influenced by many factors, and this scheme not only does not affect the final determination of group behavior but also largely reduces the serious consequences of misclassification.


### 3.2.3. Group Behavior Identification

The state of group behavior is influenced by many factors, such as the state of individual behavior in the group, the interaction between behavioral individuals, the interaction between the human body and objects, the environment, and the activity scene in which it is located [25]. In order to effectively integrate all kinds of information, this article designs a group behavior recognition process as shown in Figure 5, where the black arrows indicate the first two levels of human consistency detection and individual behavior recognition process. In order to improve the reuse rate of the feature extraction network module in the model, the spatial convolutional neural network and the motor convolutional neural network in the individual behavior recognition module are used to encode the scene context information and behavior interaction context information of group behavior and combine the recognition results of the behavior state of each actor in the group to analyze group behaviors.Figure 5
Group behavior recognition combining individual behavior and interaction scene context.From the group behavior recognition process shown in Figure5, it can be seen that the features used for group behavior recognition mainly contain three parts: the voting features of the recognition results of each individual behavior ftR, the scene context information ftSC, and the interaction context information ftIC. The interaction context information includes the interaction between each active individual in the behavioral group and the interaction between the active individual and the environment, which mainly involves the interaction motion information. We mainly use the AlexNet-based spatial convolutional neural network (SCNN) to encode the scene context information and the GoogLeNet-based motion convolutional neural network (MCNN) to encode the interaction context information in the individual behavior recognition module. The dimensions of the encoding fSC of the scene context information and the encoding fIC of the interaction context extracted using SCNN and MCNN are fixed, and the number of individual behaviors is uncertain due to the difference in the number of actors in each group behavior instance, so it becomes infeasible to directly use the recognition results of each individual behavior as part of the features for group behavior recognition. To solve this problem, this article adopts the unique thermal coding to encode the recognition results of individual behaviors and uses the voting information of each individual behavior category as the state coding of individual behaviors in group behavior recognition fR. Finally, based on these three features fR,fSC,fIC, the long short-term memory neural network is trained using equation (7) for group behavior recognition.


## 3.2.1. Multiple Human Target Detection

To address the problem of detecting the temporal consistency of multiple active human bodies in the behavior recognition scene, this article designs a network framework for a temporal consistency detection model of multiple human targets as shown in Figure3. The method first trains the human detector through the ImageNet source data domain, then migrates it to the behavior recognition scene using a dynamic modulation network to densely detect multiple active humans in the behavior video frames; then rejects those duplicate detection results by nonmaximal suppression and optimizes the detection border of active humans Bt; finally, the discriminative model conditional random field by probabilistic inference is used to match the detection results of the same active human in any two consecutive frames to achieve the temporal consistency detection of multiple human targets in the whole video sequence.Figure 3
Migration learning-based temporal consistency detection of multiple human targets.In the person detection phase as shown in Figure3, the model migrates the human target detectors from the target detection domain to the behavior recognition scenario. With sufficient samples in the source domain, assuming that the source domain-human target detection network can adapt to all situations, only some of the networks will work for a given behavior recognition scenario, and some of them are redundant and some of them even bring negative migration [21]. Without keeping the source training samples, the modulated network of this model enhances the recognition effect by the feature-adjusted selection, based on the constructed network model, using a small amount of target domain sample label information to select the effective network adaptively through the learning of weights, and suppressing the network features that cause negative migration. The migration of human detection in the target detection domain to the behavior recognition scenario is achieved by the feature maps’ weight layer, that is, the dynamic regulation of feature selection is achieved by adding a network layer that weights the feature maps, and the parameters of the feature weight layer p=p1,p2,…,pm are learned by the feedback network prediction of the following regulation network. In the training process of the human detection network, firstly, the deep convolutional network based on AlexNet in this article uses the source domain samples to train the network weights, solidify the feature extraction network, and select the feature map output from the intermediate layer; then use RPN (region proposal network) to predict the region where the human target is located, assuming that m feature map of size v1×v2 is obtained, by minimizing the equation (1) objective function to train.(1)L=12N∑i=1Nziw,p−yi2+αp1.The two network parametersω and p are trained by cross-iterative implementation, and only the feature map weighting layer and the fully connected layer are trained, which can be implemented by the standard error propagation algorithm; the training of the region prediction neural network is detailed in the Faster R-CNN; the training method of the moderation network is detailed in the adaptive human detection algorithm [22]. In the human detection stage, the candidate region is obtained by the region prediction network, and then the region modulation network feature weights are used to generate a new target detector to detect the human body in the behavior recognition scene.


## 3.2.2. Human Behavior Recognition

Individual behavior in group activities is subordinate to a part of the group behavior; in general, behavior individual will interact with other individuals or scenes in the behavior group. In addition, individual behavior is highly subjective and arbitrary, and the duration of similar behavioral activities and the magnitude of actions performed by different actors or the same actor at different moments vary greatly. Taking many factors into account, this article characterizes behaviors through the organic combination of spatial convolutional neural network and motor convolutional neural network and adopts long short-term memory neural networks to realize individual behavior recognition with no constraints on duration [23]. Due to the strong suddenness of individual behavior in group behavior and the subjective arbitrariness of behavior duration, a memory network based on long short-term memory neural networks is used to train the model of the uncertain length of input to the effective output of definite dimension, and to complete the task of effectively outputting the activity state of human behavior at any given time. As shown in Figure 4, the individual behavior recognition method with unconstrained learning duration of fused spatiotemporal features is used for behavior recognition of a behavioral individual in a group behavior scenario.Figure 4
Fusion of spatiotemporal features for learning duration unconstrained individual behavior recognition.The network model uses two types of deep convolutional neural networks to extract feature sequences of behaviors, and after extraction, the spliced feature sequences are fed into the LSTM network for memory learning. The state of individual behaviors in group behavior sometimes does not change for a long period of time, and sometimes changes frequently due to interactions with other active subjects or activity scenarios. Long short-term memory neural networks can effectively remember long or short-term information depending on the input due to the setting of forgetting gates in its network structure, and this feature of it makes it very suitable for problems faced with an uncertain duration of behavior [24]. With the long short-term memory neural network, behavioral states can be recognized at any moment. The activation function of each neuron when recognizing and training the LSTM for individual behaviors is as follows:(2)it=σWxixt+Whiht−1+bi,(3)ft=σWxfxt+Whfht−1+bf,(4)ot=σWxoxt+Whoht−1+bo,(5)gt=tanhWxcxt+Whcht−1+bc,(6)ct=ft⊙ct−1+it⊙gt,(7)ht=ot⊙tanhct.The duration of activities of individuals behaving in group behavior varies greatly. In addition, the occurrence of individual behavioral transitions is often naturally continuous and smooth, and to effectively identify behaviors, the current behavioral state must be identified within a short period of time when the behavioral transitions occur; therefore, behavioral sequences of arbitrary length need to be processed and a valid state output derived. Through testing, it is found that this unconstrained discriminative model with unconstrained input length cannot give correct judgments in time when it is insufficient to determine the current behavioral state because of the shortage of information when the input sequence is too short or the behavioral state of an individual is changed. To avoid misjudgments made by this model, in this case, the proposed memory network model does not discriminate the behavioral state when a definite determination cannot be made. The identification of individual behaviors will have some influence on the behavior of the group, but the absence of identification results for individual behaviors in the behavioral group in the short term will have little effect on them. In addition, as mentioned earlier, group behavior is influenced by many factors, and this scheme not only does not affect the final determination of group behavior but also largely reduces the serious consequences of misclassification.


## 3.2.3. Group Behavior Identification

The state of group behavior is influenced by many factors, such as the state of individual behavior in the group, the interaction between behavioral individuals, the interaction between the human body and objects, the environment, and the activity scene in which it is located [25]. In order to effectively integrate all kinds of information, this article designs a group behavior recognition process as shown in Figure 5, where the black arrows indicate the first two levels of human consistency detection and individual behavior recognition process. In order to improve the reuse rate of the feature extraction network module in the model, the spatial convolutional neural network and the motor convolutional neural network in the individual behavior recognition module are used to encode the scene context information and behavior interaction context information of group behavior and combine the recognition results of the behavior state of each actor in the group to analyze group behaviors.Figure 5
Group behavior recognition combining individual behavior and interaction scene context.From the group behavior recognition process shown in Figure5, it can be seen that the features used for group behavior recognition mainly contain three parts: the voting features of the recognition results of each individual behavior ftR, the scene context information ftSC, and the interaction context information ftIC. The interaction context information includes the interaction between each active individual in the behavioral group and the interaction between the active individual and the environment, which mainly involves the interaction motion information. We mainly use the AlexNet-based spatial convolutional neural network (SCNN) to encode the scene context information and the GoogLeNet-based motion convolutional neural network (MCNN) to encode the interaction context information in the individual behavior recognition module. The dimensions of the encoding fSC of the scene context information and the encoding fIC of the interaction context extracted using SCNN and MCNN are fixed, and the number of individual behaviors is uncertain due to the difference in the number of actors in each group behavior instance, so it becomes infeasible to directly use the recognition results of each individual behavior as part of the features for group behavior recognition. To solve this problem, this article adopts the unique thermal coding to encode the recognition results of individual behaviors and uses the voting information of each individual behavior category as the state coding of individual behaviors in group behavior recognition fR. Finally, based on these three features fR,fSC,fIC, the long short-term memory neural network is trained using equation (7) for group behavior recognition.


## 3.3. Experimental Validation and Applications

### 3.3.1. MNIST Classification Recognition Experiments

The experiment is divided into two phases. In the training phase, the MNIST handwritten digit dataset is used for testing; firstly, the initial data input is encoded and then the encoding layer changes the floating point number into a continuous pulse time series of 8 time steps, then it is convolved with 16, 32, 32, and 16 convolution kernels of all sizes 3 × 3 in turn, and finally it goes through a full linkage layer until the output.Figures6 and 7 depict the loss during the training process, as well as the accuracy variation curves tested on the validation set. Figure 6 shows the number of network training iterations on the horizontal axis and the loss on the vertical axis. The loss diminishes dramatically in the early stages of training, from 2.5 to 0.5, as shown by the graphs. After the 50th iteration, the training loss starts to decrease slowly, and after reaching the 200th iteration, the training loss no longer decreases significantly and converges to about 0.2.Figure 6
Neural network training loss curves.Figure 7
Neural network training accuracy curves.The horizontal coordinate of the graph in Figure7 is the epoch and the vertical coordinate is the accuracy tested on the validation set. After the first epoch, the accuracy has reached 65%. After the next 5 epochs, the accuracy has reached 90%, and finally after 15 epochs, the accuracy on the validation set has reached 98%.


### 3.3.2. Experiments on Volleyball Behavior Detection

In this article, the proposed hierarchical row analysis model is validated on the public dataset-volleyball dataset. In order to verify that all levels of analysis involved in the proposed hierarchical analysis model are effective for group behavior identification, experiments are conducted in this article to compare the proposed method with the set benchmark method on the volleyball dataset. Table4 shows the comparison of the recognition rate of each group behavior using the proposed method and the benchmark method on the volleyball video dataset. This experiment shows that the correct detection of human regions has an important impact on the recognition of individual behaviors in a group.Table 4
Comparison of the experimental results of the proposed method and the benchmark method on volleyball dataset.
MethodlpassrpasslsetrsetSM-CNN0.780.850.840.87Person-LSTM0.820.810.850.82Our0.870.890.880.93Table5 shows the comparison of the average recognition rates of group and individual behaviors in the volleyball video dataset using the proposed method and other classical methods. Currently, most of the recognition algorithms for group behaviors only recognize the whole group activity scene and do not analyze the individual behaviors in the group behaviors. In Table 4, the comparison with the benchmark experiments shows that considering the category information of each individual behavior that constitutes the group behavior when recognizing the group behavior can effectively improve the recognition rate of the group behavior. In Table 5, the recognition of individual behaviors by other group-as-recognition methods that analyze individual behavior recognition is shown. The comparison shows that the proposed algorithm has good effect on the recognition of individual behaviors in groups.Table 5
Comparison of group behavior and individual behavior on the volleyball dataset comparison of the average recognition rate of individual behaviors.
MethodCollectiveIndividualSM-CNN0.810.77Person-LSTM0.790.82Our0.890.84


### 3.3.3. Practical Application

Our strategy was integrated to examine the impact of social workers' actions on community public issues. We invited social workers to conduct three independent activities: patrolling, garbage collection, and neighborhood exchanges, followed by random community questionnaires. Table6 shows the findings of the questionnaires. It can be seen that simply patrolling has little effect on residents' participation in community issues; trash collection helps to raise residents' awareness of participation, but their concern for public issues remains low; and only when neighbors are acquainted with one another will people actively communicate to solve community public issues. Therefore, during the period of cultivating community social capital, social workers rebuild community trust, promote residents' interaction, and establish the process of community residents' relationship network by cultivating residents' autonomous organizations, communicating and coordinating with other community subjects in order to integrate and activate community social capital. Under the framework of social capital theory, the strategies of social workers' intervention in community public issues are summarized and refined, which mainly include: enhancing residents' motivation for community participation through rebuilding community trust; developing community relationship networks to activate the growth space of residents' motivation for participation; enhancing residents' awareness of community participation in the optimization of community interaction process; building information and resource sharing platform to develop and maintain community social capital; and establishing community. The goal of social workers' intervention in community public events is to solve public problems, while the process goal is to build and improve the trust, interaction, norms, and relational network capital of community residents. Through the three major working techniques of social workers, the social workers innovate residents' interaction mechanisms, build a platform for residents' communication and cooperation, cultivate residents' organizations, and mobilize community social capital in order to increase residents' awareness of community autonomy, strengthen their motivation for community participation, and achieve the effect of intervention goals.Table 6
Effect of social worker's behavior on residents' attention.
Social work behaviorContinuous attention (%)Occasional attention (%)Patrols46.1825.36Trash removal55.3732.54Neighborhood exchange71.4350.86


## 3.3.1. MNIST Classification Recognition Experiments

The experiment is divided into two phases. In the training phase, the MNIST handwritten digit dataset is used for testing; firstly, the initial data input is encoded and then the encoding layer changes the floating point number into a continuous pulse time series of 8 time steps, then it is convolved with 16, 32, 32, and 16 convolution kernels of all sizes 3 × 3 in turn, and finally it goes through a full linkage layer until the output.Figures6 and 7 depict the loss during the training process, as well as the accuracy variation curves tested on the validation set. Figure 6 shows the number of network training iterations on the horizontal axis and the loss on the vertical axis. The loss diminishes dramatically in the early stages of training, from 2.5 to 0.5, as shown by the graphs. After the 50th iteration, the training loss starts to decrease slowly, and after reaching the 200th iteration, the training loss no longer decreases significantly and converges to about 0.2.Figure 6
Neural network training loss curves.Figure 7
Neural network training accuracy curves.The horizontal coordinate of the graph in Figure7 is the epoch and the vertical coordinate is the accuracy tested on the validation set. After the first epoch, the accuracy has reached 65%. After the next 5 epochs, the accuracy has reached 90%, and finally after 15 epochs, the accuracy on the validation set has reached 98%.


## 3.3.2. Experiments on Volleyball Behavior Detection

In this article, the proposed hierarchical row analysis model is validated on the public dataset-volleyball dataset. In order to verify that all levels of analysis involved in the proposed hierarchical analysis model are effective for group behavior identification, experiments are conducted in this article to compare the proposed method with the set benchmark method on the volleyball dataset. Table4 shows the comparison of the recognition rate of each group behavior using the proposed method and the benchmark method on the volleyball video dataset. This experiment shows that the correct detection of human regions has an important impact on the recognition of individual behaviors in a group.Table 4
Comparison of the experimental results of the proposed method and the benchmark method on volleyball dataset.
MethodlpassrpasslsetrsetSM-CNN0.780.850.840.87Person-LSTM0.820.810.850.82Our0.870.890.880.93Table5 shows the comparison of the average recognition rates of group and individual behaviors in the volleyball video dataset using the proposed method and other classical methods. Currently, most of the recognition algorithms for group behaviors only recognize the whole group activity scene and do not analyze the individual behaviors in the group behaviors. In Table 4, the comparison with the benchmark experiments shows that considering the category information of each individual behavior that constitutes the group behavior when recognizing the group behavior can effectively improve the recognition rate of the group behavior. In Table 5, the recognition of individual behaviors by other group-as-recognition methods that analyze individual behavior recognition is shown. The comparison shows that the proposed algorithm has good effect on the recognition of individual behaviors in groups.Table 5
Comparison of group behavior and individual behavior on the volleyball dataset comparison of the average recognition rate of individual behaviors.
MethodCollectiveIndividualSM-CNN0.810.77Person-LSTM0.790.82Our0.890.84


## 3.3.3. Practical Application

Our strategy was integrated to examine the impact of social workers' actions on community public issues. We invited social workers to conduct three independent activities: patrolling, garbage collection, and neighborhood exchanges, followed by random community questionnaires. Table6 shows the findings of the questionnaires. It can be seen that simply patrolling has little effect on residents' participation in community issues; trash collection helps to raise residents' awareness of participation, but their concern for public issues remains low; and only when neighbors are acquainted with one another will people actively communicate to solve community public issues. Therefore, during the period of cultivating community social capital, social workers rebuild community trust, promote residents' interaction, and establish the process of community residents' relationship network by cultivating residents' autonomous organizations, communicating and coordinating with other community subjects in order to integrate and activate community social capital. Under the framework of social capital theory, the strategies of social workers' intervention in community public issues are summarized and refined, which mainly include: enhancing residents' motivation for community participation through rebuilding community trust; developing community relationship networks to activate the growth space of residents' motivation for participation; enhancing residents' awareness of community participation in the optimization of community interaction process; building information and resource sharing platform to develop and maintain community social capital; and establishing community. The goal of social workers' intervention in community public events is to solve public problems, while the process goal is to build and improve the trust, interaction, norms, and relational network capital of community residents. Through the three major working techniques of social workers, the social workers innovate residents' interaction mechanisms, build a platform for residents' communication and cooperation, cultivate residents' organizations, and mobilize community social capital in order to increase residents' awareness of community autonomy, strengthen their motivation for community participation, and achieve the effect of intervention goals.Table 6
Effect of social worker's behavior on residents' attention.
Social work behaviorContinuous attention (%)Occasional attention (%)Patrols46.1825.36Trash removal55.3732.54Neighborhood exchange71.4350.86


## 4. Conclusions

The macrolevel of social workers' participation in community governance has gained a lot of attention, whereas the microlevel of social workers' intervention in community public concerns has gained less attention. Community public problems are the concentrated expression of contradictions and conflicts in metropolitan communities, and they have a significant impact on societal peace and stability. To investigate the impact of social workers' behavior on community public problems, researchers first used transfer learning to achieve temporal consistency detection of multiple human targets, and then used self-regulated networks to solve the problem of negative migration of samples in the transfer learning process, which solves the problem of human detection in behavior recognition scenarios with insufficient behavioral human detection samples and labeling information. For the problem of inconsistent duration of individual behaviors caused by subjective arbitrariness of individual behaviors in group behaviors, this article achieves effective recognition of individual behaviors with unconstrained duration based on LSTM using spatiotemporal features. For the recognition of group behaviors, this article reuses SCNN and MCNN to capture the contextual information of scenes and behavioral interactions and integrates all kinds of valid information to achieve the effective recognition of group behaviors. Then, the study of social workers' behaviors on residents' willingness to participate in community public issues clarifies that social workers should clearly recognize that the power of community residents is huge when intervening in community public issues, and the more residents are mobilized, the social resources that can be strived for keep expanding. Community space will continue to adjust along with the relevance of community problem-solving, and social capital will expand and overlap. The proliferation of interaction reciprocity network will further promote the productivity of social capital, improve residents' community participation ability and sense of participation effectiveness, and help promote community residents' autonomy.



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*Source: 7399600-2022-05-23.xml*