<p>Effective legal case retrieval is critical for fair and just adjudication of similar cases. Unfortunately, current methods fall short of understanding legal cases at a semantic level. To address the issue, this paper presents a BERT-based two-stage ranking model for Chinese legal case retrieval. The first stage uses the well-known BM25 ranking function to quickly retrieve top <i>n</i> case candidates from the candidate pool. In the second stage, firstly the fine-tuned BERT is used for semantic analysis of the retrieved case candidates (<i>i.e.</i>, obtained their embeddings) and then a ranking mechanism is used to re-rank the retrieved cases. We fine-tune the BERT model by integrating the point-wise method with auxiliary learning to better understand the deep semantics of a legal judgement text, and we further train the re-ranking neural network based on the fine-tuned BERT model in conjunction with the pair-wise method. Our extensive experiments reveal that the proposed method surpasses state-of-the-art methods on the Legal Case Retrieval Dataset (LeCaRD) by improving the ranking quality of retrieved legal cases, leading to more relevant cases appearing higher in the ranked results.</p>