With the increasing precision requirements for integrated circuit testing, the long-term operational stability of the test equipment has become a key factor affecting test quality and cost. Traditional monitoring methods based on fixed confidence-level thresholds, due to their static nature, struggle to adapt to the performance drift caused by aging and environmental fluctuations during long-term operation. As a result, they often suffer from false alarms or missed detections, which hinders the implementation of predictive maintenance. To address this issue, a dynamic threshold optimization monitoring method integrating Gaussian mixture model (GMM) and Kullback-Leibler divergence (KL divergence) is proposed. This method first uses a GMM to accurately model the multimodal distribution of test data, effectively characterizing the equipment′s operational state under complex multi-condition scenarios. It then introduces KL Divergence to quantify in real-time the distribution difference between the monitoring data and the healthy baseline model. Building on this, it innovatively updates the anomaly detection threshold based on a rolling historical KL Divergence sequence, allowing the threshold to adaptively adjust according to the natural drift in data distribution. This mechanism fundamentally overcomes the mismatch between static thresholds and dynamic processes, enhancing the monitoring system′s sensitivity to both gradual performance degradation and sudden anomalies. Experimental results show that compared to the traditional K-means clustering method with a fixed threshold, the proposed method achieves significant improvements in both anomaly detection accuracy and F1-score, enabling more sensitive and reliable identification of performance fluctuations and early-stage faults in test equipment. This method not only provides an effective technical solution for stability monitoring and predictive maintenance of integrated circuit test equipment, but also holds potential for extension to other industrial equipment health management fields due to its core framework of sensing distribution changes through probabilistic modeling and achieving threshold self-adaptation. This framework demonstrates strong generalizability, offering a new approach with continuous adaptation capabilities for equipment condition monitoring in dynamic operational environments.