A Deep Learning-Based Model for Analyzing Social Public Issues

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.