<p>Legal Case Retrieval not only involves the identification and selection of similar cases, but also encompasses an in-depth comparison and abstraction of their holdings, reasoning structures, and underlying legal principles, serving as a critical pillar of judicial digital transformation and the development of smart courts. With the rapid advancement of neural networks, deep learning, and pretrained language models, retrieval paradigms have gradually evolved from traditional keyword matching toward intelligent approaches such as semantics-based dense retrieval. Owing to the high level of domain specificity and structural complexity of legal documents, case-based retrieval has long remained a focal research topic in this field. To leverage the strengths of traditional methods in capturing global semantics while fully exploiting the contextual modeling capabilities of pretrained language models, this paper proposes ALTER, A Lightweight Topic-awarE Representation legal case retrieval system. Specifically, ALTER incorporates topic modeling to enhance the global semantic representations learned by pretrained language models, and introduces a Co-Attention layer to explicitly facilitate bidirectional interactions between the topic encoder and the text encoder. In addition, a multi-label classification task is employed to guide the text encoder to implicitly learn topical information, resulting in topic-aware text representations. During the online retrieval stage, only the text encoder is used to construct the index, enabling efficient and lightweight inference while achieving joint matching between text and topics. Experimental results on the Chinese LCR dataset LeCaRD and the English LCR dataset COLIEE23-24 demonstrate that ALTER consistently achieves state-of-the-art performance across multiple evaluation metrics.</p>