The existing traditional Chinese medicine prescription recommendation methods mostly express the multi-dimensional relationship between symptoms and herbs by constructing symptom herb, symptom symptom and herb herb multi graph structures, but mostly ignore the semantic interaction between symptoms and herbs, and the lack of effective multi view feature fusion strategies leads to insufficient extraction of important information and the neglect of the relative importance of symptoms leading to inaccurate syndrome induction. Therefore, a traditional Chinese medicine prescription recommendation model based on semantic knowledge integration and multi view integration was proposed. First, the BERT(bidirectional encoder representations from transformers) model was introduced as the semantic encoder of node words to learn the semantic interaction information between symptoms and herbs, and enhance the model's ability to express node semantic features. Secondly, multi view feature fusion module was designed, which used multi head attention mechanism and low rank matrix decomposition technology to generate feature conversion matrix between views, and adaptively and dynamically fused information of different views. Finally, attention mechanism was used to accurately summarize the syndrome of specific symptom set to optimize the effect of prescription recommendation. The experimental results show that the key indicators of this model on the public dataset F₁@5, F₁@10, F₁@20 is 10.27%, 15.10% and 17.29% higher than the optimal baseline model, which proves its effectiveness.