Chinese medicine (CM) diagnosis intellectualization is one of the hotspots in the research of CM modernization. The traditional CM intelligent diagnosis models transform the CM diagnosis issues into classification issues

however

it is difficult to solve the problems such as excessive or similar categories. With the development of natural language processing techniques

text generation technique has become increasingly mature. In this study

we aimed to establish the CM diagnosis generation model by transforming the CM diagnosis issues into text generation issues. The semantic context characteristic learning capacity was enhanced referring to Bidirectional Long Short-Term Memory (BILSTM) with Transformer as the backbone network. Meanwhile

the CM diagnosis generation model Knowledge Graph Enhanced Transformer (KGET) was established by introducing the knowledge in medical field to enhance the inferential capability. The KGET model was established based on 566 CM case texts

and was compared with the classic text generation models including Long Short-Term Memory sequence-to-sequence (LSTM-seq2seq)

Bidirectional and Auto-Regression Transformer (BART)

and Chinese Pre-trained Unbalanced Transformer (CPT)

so as to analyze the model manifestations. Finally

the ablation experiments were performed to explore the influence of the optimized part on the KGET model. The results of Bilingual Evaluation Understudy (BLEU)

Recall-Oriented Understudy for Gisting Evaluation 1 (ROUGE1)

ROUGE2 and Edit distance of KGET model were 45.85

73.93

54.59 and 7.12

respectively in this study. Compared with LSTM-seq2seq

BART and CPT models

the KGET model was higher in BLEU

ROUGE1 and ROUGE2 by 6.00–17.09

1.65–9.39 and 0.51–17.62

respectively

and lower in Edit distance by 0.47–3.21. The ablation experiment results revealed that introduction of BILSTM model and prior knowledge could significantly increase the model performance. Additionally

the manual assessment indicated that the CM diagnosis results of the KGET model used in this study were highly consistent with the practical diagnosis results. In conclusion

text generation technology can be effectively applied to CM diagnostic modeling. It can effectively avoid the problem of poor diagnostic performance caused by excessive and similar categories in traditional CM diagnostic classification models. CM diagnostic text generation technology has broad application prospects in the future.