Abstract: The present invention provides a system and method for analysis of integrated circuit testing anomalies based on deep learning. Through repeated training by deep learning with historical test data accumulated during testing, automatic optimization of parameter settings depending on learning and training conditions is made possible. Moreover, based on real-time test data, testing anomalies can be predicted and early warnings against them can be provided to allow advanced intervention for preventing their occurrence. Additionally, for testing anomalies that have occurred, solutions can be automatically identified and provided, which shorten the times taken by different technicians to address the anomalies, resulting in more effective utilization of the equipment and lower testing cost.