Abstract: In order to improve the efficiency of doctor-patient dialogue by enabling doctors to quickly propose reasonable rhetorical questions in intelligent consultation, a method of rhetorical question generation based on deep neural network was proposed. Firstly, a large number of doctor-patient dialogue texts were obtained and labeled. Then, two classification models, Text Recurrent Neural Networks (TextRNN) and Text Convolutional Neural Networks (TextCNN), were used to classify doctor's statements respectively. Then, Text Recurrent Neural Networks-BLSTM (TextRNN-B) and Bidirectional Encoder Representations from Transformers(BERT) classification models were used to trigger questions. Six different Q&A selection methods were designed to simulate the situation in the field of medical consultation. Then, Open-Source Neural Machine Translation (OpenNMT) model was used to generate rhetorical questions. Finally, the generated rhetorical questions were evaluated comprehensively. Experimental results show that TextRNN is better than TextCNN in classification, and BERT model is better than TextRNN-B in question triggering. When OpenNMT model is used to realize rhetorical question generation in Window-top mode, the best results are obtained by using two evaluation indexes: Bilingual Evaluation Understudy (BLEU) and Perplexity (PPL). The proposed method verifies effectiveness of deep neural network technology in the generation of rhetorical question, which can effectively solve the problem of doctor-patient question generation.