Within current task-oriented dialogue systems, the focus of intent detection predominantly centers on closed domains. Nevertheless, in real-world usage scenarios, a substantial proportion of interactions fall into the open-domain category. User intentions frequently transcend predefined boundaries, giving rise to a multitude of out-of-domain intents, which pose a formidable challenge to existing models, ultimately leading to diminished recognition rates and accuracy. The demand for open intent detection models is increasing in today's society to address this issue effectively. This paper proposes a method to optimize datasets, thereby enhancing the training accuracy of open intent detection models. Specifically, this paper employs the Adaptive Decision Boundary Learning algorithm, which is currently popular in open intent detection. Leveraging this algorithm, this paper suggests using the K-means clustering algorithm to refine the intent labels within the dataset. This process helps identify and remove outliers in the dataset, making the distinction between known domain and open-domain intent labels more precise. Experimental results on two datasets, banking77 and stackoverflow, demonstrate the effectiveness of our approach in significantly improving model accuracy.