Research on the Ancient Chinese Semantic Database Model Based on Machine Learning

This paper proposes an ancient Chinese semantic database model based on LSTM, which aims to solve the problems of complex grammatical structure and semantic ambiguity in ancient Chinese. By introducing LSTM and attention mechanism, this study achieves efficient modeling of semantic relations in ancient Chinese texts. The experiment uses a self-built ancient Chinese corpus, covering classic literature and comparing the model with traditional rule-based methods. Experiments indicate that LSTM model can increase the precision of semantic analysis by 15%, recall 10%, and F1 12%, especially in dealing with polysemy and synonyms. The LSTM and attention mechanism can improve the performance of semantic recognition, syntactic relation extraction, and it is more accurate and robust. Through visual analysis, the model can better capture long-distance grammatical dependencies and effectively improve the accuracy of semantic reasoning. Despite this, the model still faces certain challenges when processing some rare words. This study offers a new way to analyze the meaning of ancient Chinese, and offers a feasible path for further studies in relevant areas.