In response to the growing complexity and performance of Integrated Circuit (IC), there is an urgent need to enhance the testing and stability of IC test equipment. A method was proposed to predict equipment stability using the upper side boundary value of normal distribution. Initially, the K-means clustering algorithm classifies and analyzes sample data. The accuracy of this boundary value is compared under two common confidence levels to select the optimal threshold. A range is then defined to categorize unqualified test data. Through experimental verification, the method achieves the purpose of measuring the stability of qualitative IC equipment through a deterministic threshold value and judging the stability of the equipment by comparing the number of unqualified data with the threshold value, which realizes the goal of long-term operation monitoring and stability analysis of IC test equipment.