This paper presents MaRSI, an automatic subject indexing method designed to address the limitations of traditional manual indexing and emerging GenAI technologies. Focusing on improving indexing accuracy in cross-lingual contexts and balancing efficiency and accuracy in large-scale datasets, MaRSI mimics human reference learning behavior by constructing semantic indexes from pre-indexed document. It calculates similarity to retrieve relevant references, merges, and reorders their topics to generate index results. Experiments demonstrate that MaRSI outperforms supervised fine-tuning of LLMs on the same dataset, offering advantages in speed, effectiveness, and interpretability.