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with the rapid improvement of the information level of Missile equipment, the requirements for intelligent application of operation and support are increasing. Therefore, how to quickly process the data information accumulated by Missile equipment in the process of long-term use, storage and maintenance, and improve the data quality is the primary research content under the background of data time and the fundamental way of intelligent application of data. Based on the big data technology, this paper analyzes the detection method of test data anomaly domain by means of statistical theory and deep learning method, divides the test data anomaly domain into anomaly points, anomaly curves and anomaly clusters, and around the test data and data characteristics of Missile equipment, detail the detection method principle detection model, algorithm steps of test data anomaly domain under different characteristic data conditions,, and simulate the typical test data sequence of equipment to carry out application technology research.