This paper describes YNU-HPCC(Alias JH) team’s participation in the sub-task 2 of the SemEval-2025 Task 5, which requires fine-tuning language models to align subject tags with the TIBKAT collection. The task presents three key challenges: cross-disciplinary document coverage, bilingual (English-German) processing requirements, and extreme classification over 200,000 GND Subjects. To address these challenges, we apply a contrastive learning framework using multilingual Sentence-BERT models, implementing two innovative training strategies: mixed-negative multi-label sampling, and single-label sampling with random negative selection. Our best-performing model achieves significant improvements of 28.6% in average recall, reaching 0.2252 on the core-test set and 0.1677 on the all-test set. Notably, we reveal model architecture-dependent response patterns: MiniLM-series models benefit from multi-label training (+33.5% zero-shot recall), while mpnet variants excel with single-label approaches (+230.3% zero-shot recall). The study further demonstrates the effectiveness of contrastive learning for multilingual semantic alignment in low-resource scenarios, providing insights for extreme classification tasks.