The rapid development of society brings more and more pressure to people. Due to work pressure and self-problems, more and more people are eating three meals irregularly and unhealthily, which leads to the growing population of people suffering from digestive diseases. When the body just appears abnormal, most people first will choose to find information on the Internet. Due to the limitations of traditional search engines, the process is time consuming, and because of the diversity of diseases, it is difficult for users to accurately obtain relevant information. In view of this problem, considering that the dialogue system is a more advanced information retrieval system. This study explores a generative dialogue system suitable for the field of gastroentology, using support vector machine and active learning to obtain professional consultation dialogue corpus of gastroenterology on multiple medical websites. Labor and statistics are combined to build a professional dictionary of diseases, drugs, and symptoms of digestive diseases, word segmentation is improved in the medical field. On this basis, multi-Encoder and multi-Decoder structure is combined with gated loop unit GRU, the attention mechanism is also added, then the model strengthening training method is proposed, which combines reverse input and key-value pair vector and Word2Vec vector, to obtain the final model. The experimental results show that the word segmentation result is much higher than the traditional method, and the resulting dialogue model can effectively generate the sentence related to the question, which can improve the answer accuracy of the dialogue system.