## 7 Conclusions  
In this study, a hybrid MacBERT-BiLSTM-based architecture (BLAF) is proposed to systematically address the key challenges in the disambiguation task of Chinese homophones through the synergistic design of dynamic feature fusion and sequence-aware attention mechanisms. The model innovatively combines the global semantic representation capability of pre-trained language models with the local sequence-dependent modeling advantage of BiLSTM, and introduces an adapter layer to achieve the efficient alignment of heterogeneous feature spaces. Experiments have confirmed that the performance improvement of BLAF in homophone disambiguation primarily stems from the following: (1) the tone transfer patterns captured by BiLSTM (such as tone sandhi in third-tone consecutive syllables) increase the contextual distinguishability of homophonic pairs by 37.6%; (2) the adapter layer reduces the cosine alignment loss between pinyin embeddings and character embeddings to 0.15 \(\pm\) 0.03 (baseline: 0.42 \(\pm\) 0.11), promoting cross-modal representation fusion. In the SIGHAN 2015 (data fine-tuning and supplementation) benchmark test, the model achieved the current optimal performance with 93.37% accuracy and 93.25% F1 score, and the inference latency was reduced to 8.7 milliseconds, which is a 15.74% accuracy improvement over the pure BERT baseline model. Ablation experiments validated the effectiveness of the core components: the BiLSTM layer contributes 7.69% F1 gain by capturing character-level error propagation patterns, while the adapter layer with the staged training strategy improves the fusion accuracy by 5.58% and reduces the risk of overfitting by 8.5%, respectively. In addition, this system realizes a systematic breakthrough in Chinese homophone disambiguation technology through the deep synergy of the sensor-driven environment adaptive mechanism and the MacBERT-BiLSTM model. The architecture innovatively cross-modally aligns the noise spectral features (60 s temporal window) collected by acoustic sensors in real time with textual semantic features to construct an environment-aware dynamic dictionary (EDL), and the LSTM-based dynamic acoustic compensation mechanism (DAC) constructs a Time-Varying Spectral Mask (TSVM) by analyzing the sensor data to effectively improve the band selectivity of the Mel filter bank, so that the signal-to-noise ratio (SNR) of the recognition module reaches 18.6 dB, which reduces the homophone misrecognition rate from the source. Combined with Paraformer-zh-large's non-autoregressive decoding strategy and CIF streaming mechanism, the system meets the demands of industrial-grade real-time interaction. The architecture proposed in this study breaks through the limitations of traditional methods through three dimensions: first, the dynamic layer fusion strategy achieves adaptive weighting of Transformer and LSTM outputs through a gated attention mechanism; second, the sequence-aware attention module constructs a cross-dimensional weight assignment model to make the modeling of long-distance dependencies efficient; and, lastly, the industrial optimization scheme (mixed-accuracy training and rule engine integration) reduces GPU memory consumption while maintaining model performance. These innovations enable the model to maintain high accuracy in complex acoustic environments (75 dB noise) and construct a multimodal error correction decision tree through the synergistic analysis of sensor data streams and text editing distances. Despite the significant progress, two challenges remain in this study: first, the model's ability to generalize to low-resource domains (e.g., dialects and technical terms) is limited; second, the existing context window (\(\leq\)512 characters) is difficult to support chapter-level semantic reasoning requirements.