In the natural human-computer dialogue system, environmental noises, accents and some other factors may cause the speech recognition errors which leads to computers' error responses to human. The dialogs are often interrupted by the system's bad responses. Three types of human computer interruptions are considered in this paper:improper feedback for topic, improper response for a vague user query, and improper feedback for an exact user query. According to the records of the user and computer dialogue analysis, the interruptions caused by three situations above are compared and used to analyze the importance of intention classification in human-computer conversation. The statistical data find that the dialogue interruption caused by the inappropriate topic feedback is the most obvious problem, amounting to 60.1%. Under the correct feedback of the topic, the interrupt ratio of the subject caused by accurate answer and fuzzy answer is 22.2% and 21.6% respectively. In the case of error speech recognition, semantic analysis can bring more feedback error to the error of speech recognition. The analysis of experimental data shows that the speech recognition errors, can effectively reduce the man-machine conversation interrupt and improve the naturalness of human-computer dialogue system according to the context information to improve the accuracy of the computer on the topic of user feedback,. This paper provides the importance of intention classification in human machine dialogue, which helps to improve the performance of human-computer dialogue system.