To address the difficulty of word boundary recognition stemming from nested entities in Chinese medical texts， as well as significant semantic information loss in existing Lattice-LSTM structures with integrated lexical features， an adaptive lexical information enhancement model for Chinese Medical Named Entity Recognition （MNER） was proposed. First， the BiLSTM （Bi-directional Long-Short Term Memory） network was utilized to encode the contextual information of the character sequence and capture the long-distance dependencies. Next， potential word information of each character was modeled as character-word pairs， and the self-attention mechanism was utilized to realize internal interactions between different words. Finally， a lexicon adapter based on bilinear-attention mechanism was used to integrate lexical information into each character in the text sequence， enhancing semantic information effectively while fully utilizing the rich boundary information of words and suppressing words with low correlation. Experimental results demonstrate that the average F1 value of the proposed model increases by 1.37 to 2.38 percentage points compared to the character-based baseline model， and its performance is further optimized when combined with BERT.