A Comparative Study of Community Service Work between China and the United States Based on Deep Learning

This paper proposes a deep learning-based computational framework for comparative studies of community social work in China and the United States. Traditional comparative research predominantly relies on qualitative case analysis, which presents limitations in identifying macro-level, systematic patterns. To address this gap, we develop an integrated analytical framework leveraging deep learning models—including natural language processing, graph neural networks, and computer vision—to mine multi-source heterogeneous data such as community work texts, social networks, and community imagery from both countries. Through automated clustering of practice patterns, in-depth perception of community needs, and quantitative evaluation of intervention effectiveness, this study seeks to move beyond superficial descriptions and uncover similarities and differences in institutional logics, practice pathways, and cultural contexts between the two systems. By doing so, it aims to contribute a new paradigm for knowledge production and methodological innovation in social work within the context of globalization.