<p>Named Entity Recognition (NER) is a crucial task for extracting information in Chinese literature text, often employing Chinese character-based sequence labeling methods. However, existing methods struggle with accurately identifying literature-specific terms due to the prevalence of specialized terminology and insufficient utilization of term type information, leading to ambiguous entity boundaries and misclassifications. To address these limitations, we developed literature word vectors and constructed literature dictionaries incorporating term type information. We introduce a type representation-enhanced model, wherein characters, corresponding terms, and their types are fed into a Transformer encoder for interaction and encoding. This process yields contextually informed type-enhanced character representations. Subsequently, these representations are combined with the type representations of corresponding terms using a gating mechanism before being forwarded to a CRF for decoding and obtaining entity tags. We evaluated our model on the named entity recognition dataset for Chinese literature text (Chinese SanWen), demonstrating significant improvements over competing methods.</p>