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.
References
[1]
Ardichvili, A. (2013). Learning and Knowledge Sharing in Virtual Communities of Practice: Motivators, Barriers, and Enablers. AdvancesinDevelopingHumanResources,10, 541-554. https://doi.org/10.1177/1523422308319536
[2]
Ataizi, M. (2012). Communities of Practice. In N. M. Seel (Ed.), EncyclopediaoftheSciencesofLearning (pp. 654-658). Springer US. https://doi.org/10.1007/978-1-4419-1428-6_2075
[3]
Borzillo, S., Aznar, S., & Schmitt, A. (2011). A Journey through Communities of Practice: How and Why Members Move from the Periphery to the Core. EuropeanManagementJournal,29, 25-42. https://doi.org/10.1016/j.emj.2010.08.004
[4]
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R. et al. (2022). Recent Advances and Applications of Deep Learning Methods in Materials Science. npjComputationalMaterials,8, Article No. 59. https://doi.org/10.1038/s41524-022-00734-6
[5]
Deze, J., Ranogajec, L., & Sudaric, T. (2017). Development of Local Communities through the Production of Regional Indigenous Products. Interdisciplinary Management Research, 13, 1149-1164.
[6]
Duguid, P. (2005). “The Art of Knowing”: Social and Tacit Dimensions of Knowledge and the Limits of the Community of Practice. TheInformationSociety,21, 109-118. https://doi.org/10.1080/01972240590925311
[7]
Fordham, A. E., & Robinson, G. M. (2019). Identifying the Social Values Driving Corporate Social Responsibility. SustainabilityScience,14, 1409-1424. https://doi.org/10.1007/s11625-019-00720-w
[8]
Leal Filho, W., Levesque, V., Sivapalan, S., Salvia, A. L., Fritzen, B., Deckert, R. et al. (2022). Social Values and Sustainable Development: Community Experiences. EnvironmentalSciencesEurope,34, Article No. 67. https://doi.org/10.1186/s12302-022-00641-z
[9]
Mienye, I. D., & Swart, T. G. (2024). A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications. Information,15, Article 755. https://doi.org/10.3390/info15120755
[10]
Milton, D., Bryant, A., Appleton, P. R., & Duda, J. L. (2025). Using the Principles from Community of Practice: Developing Sustainable Professional Development Programmes in Physical Education. EducationSciences,15, Article 713. https://doi.org/10.3390/educsci15060713
[11]
Schmidhuber, J. (2015). Deep Learning in Neural Networks: An Overview. NeuralNetworks,61, 85-117. https://doi.org/10.1016/j.neunet.2014.09.003
[12]
Shiri, F. M., Perumal, T., Mustapha, N., & Mohamed, R. (2024). A Comprehensive Overview and Comparative Analysis on Deep Learning Models. JournalonArtificialIntelligence,6, 301-360. https://doi.org/10.32604/jai.2024.054314
