Exploration of Data Mining Methodologies and Applications in Social Work

With the deepening of digital governance, traditional social work is undergoing a critical transition from experience-dependence to a data-driven paradigm. This study aims to explore the application paths and efficacy of data mining technologies within the field of social work. Firstly, the paper systematically reviews the specific application scenarios of classification algorithms, Transformer models, and Convolutional Neural Networks (CNN) in risk prediction for vulnerable groups, client profiling, and text analysis of case records. It demonstrates that data mining methods can significantly enhance the predictability of social services and the efficiency of resource allocation. However, technical intervention also presents challenges such as algorithmic bias, data privacy concerns, and conflicts with professional ethics. Consequently, this paper proposes the establishment of a "technology-ethics" dual-axis digital social work framework, ensuring that while data mining improves professional efficiency, it does not undermine the humanistic essence of social work.