Few-shot relation extraction (RE) is a critical task in natural language processing (NLP), yet benchmark datasets for non-English languages, particularly Chinese, remain scarce. Owing to their narrow focus and insufficient coverage of relation types, extant Chinese datasets typically exhibit limited applicability and it proves difficult to establish a standardized evaluation benchmark. To address this issue, we introduce FREDC, a few-shot RE dataset for Chinese (FREDC) featuring nearly 100 relation categories across general, medical, and financial domains, with over 100,000 annotated instances. FREDC supports diverse experimental setups, including cross-domain and “None-of-the-Above” (NOTA) tasks. To evaluate FREDC, we adapted the baseline RE model designed for English to Chinese and tested across multiple experimental configurations. FREDC has proven to be effective across multiple models. It also posed challenges to cross-domain and NOTA tasks, due to the linguistic complexity and domain-specific features of the dataset. Comparisons with English datasets showed a performance gap of 15–30% on the accuracy. FREDC fills a critical gap by providing a standardized, high-quality resource for Chinese few-shot RE. It offers a robust foundation for advancing multilingual RE research and improving the adaptability of RE models across languages and domains. We provide relevant code snippets to facilitate reproducibility and encourage further research in the field.