Automated tag prediction for competitive programming problems is critical in organizing problem archives, enhancing personalized learning and efficient problem-solving. However, existing approaches are hindered by inconsistent labeling, severe tag imbalances, and reliance on large, general-purpose models with high computational costs. This work addresses these issues by integrating domain adaptation techniques into transformer-based architectures to generate high-quality, domain-specific embeddings. We first construct a comprehensive dataset from Codeforces contests, comprising over 7,000 problems with rich metadata. We employ a multi-tiered preprocessing strategy to mitigate tag imbalance and improve textual input quality. Our experiments evaluate three classifier architectures: a chain classifier that leverages inter-label dependencies, a One-vs-All framework with independent transformer-based classifiers, and a unified multi-label model. Comparative analyses against standard training paradigms and large models (including GPT4o, GPT4o-mini, and o1-mini) demonstrate that our domain-adapted approach achieves significant improvements, with gains in F1 and AUC of up to 5–10 percentage points in key subsets of tags. Our findings confirm that tailored domain adaptation and strategic data preprocessing can bridge the performance gap between generic large-scale language models and specialized systems for competitive programming. Our code and data are public at https://github.com/DinuGeorge0019/MLCP .