English deep semantic understanding and generation technology integrating knowledge graph and natural language processing

At present, natural language generation models have made certain progress, but existing methods still face challenges in maintaining logical consistency, integrating specific domain knowledge, and adapting to cross-domain scenarios.Hence, this paper proposes an optimisation framework that integrates knowledge graph and deep generation model.By introducing rich semantic information from the knowledge graph in the model, the logical consistency, knowledge and linguistic diversity of the generated content are enhanced.This paper conducts an experimental comparison of four models: knowledge-enhanced bidirectional encoder representation from transformers (K-BERT), graph-to-sequence model (Graph2Seq), Cox's proportional hazards model (ST-CoKE), and an optimised model.The experimental results show that the proposed optimised model outperforms the comparison model in several indicators, especially in contextual understanding, knowledge coverage and language diversity.This paper made up for the deficiency of current natural language generation technology in knowledge integration and provided new ideas for solving semantic fault problems.