## 1 Introduction
Ultimately, these innovations enhance the accuracy of slot entity extraction in medical texts. It can be seen that there is relatively little research in the medical dialogue field regarding the extraction of intents and slots from dialogue texts [8, 9, 10, 11, 12]. However, during Chinese medical dialogues, there are serious issues, such as multiple meanings of words in the text. If the intent and slot information in the patient's consultation text are not accurately identified, it will affect the correctness of the diagnostic texts generated by subsequent medical dialogue systems, thereby reducing the effectiveness of medical consultations. Take [11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 4  
consultation text will be greatly biased. In addition, in Chinese medical dialogues, there are cases where character errors lead to difficulties in entity extraction, potentially resulting in the omission of crucial label information. Take for example, '(Can fish glycerol be eaten together?). The slot label of this sentence should be '(Can fish glycerol be eaten together?). Because of the error of the word ', the system may not be able to extract the label ', which may mislead the medical dialogue system to give wrong diagnosis results. Therefore, accurately identifying the patient's diagnostic intent and key entity information in the dialogue text forms the foundation for achieving intelligent medical dialogue. To address these issues, this paper proposes a character-word information interaction framework for natural language understanding in the Chinese medical dialogue domain. For intent detection, the goal is to capture the most relevant features of discourse intents, mitigate the effects of multiple meanings of words on intent judgments, and improve the accuracy of detecting patient intents in Chinese medical dialogues. For slot filling tasks, integrating character and word information enhances the model's ability to capture crucial label information in Chinese medical dialogues. Figure 2 illustrates the flowchart depicting the key contribution of this paper. In summary, the contribution of the work in this paper is as follows:  
1. This paper proposes a character-word information interaction framework for natural language understanding in the Chinese medical dialogue domain, which integrates the characteristics of character and word information to accurately achieve intent detection and slot filling in the Chinese medical dialogue domain. 2. For intent detection, this paper develops the intent information adapter designed to mitigate the impact of multiple meanings of word on intent assessment. For the slot filling task, the paper proposes the slot label extractor to address challenges arising from character errors that complicate the extraction of slot entities. 3. In this paper, experiments were conducted on the Chinese medical dataset and public dataset, respectively. The results demonstrate that our model achieves accurate intent detection and slot filling for medical dialogue texts compared with other baseline models. The rest of the article is organized as follows. Section 2 provides an overview of some work in the area of Chinese medical dialogue and joint work on intent detection and slot filling tasks. Section 3 describes the character-word information interaction framework proposed in this paper, as well as the proposed intent information adapter and slot label extractor and how they are jointly trained. Section 4 describes the dataset, assessment metrics, experimental parameters, baseline, and analyses of the experimental component. In the final section, the research is summarized. Figure 2: Challenge-contribution graph. ‘ means clear nose is just a little bit, and ‘ means can fish glycerol be eaten together?