X-Ray powder Diffraction (XRD) is an exact instrument for material research with complex physical structure. Thus, a small hardware deviation will affect the quality of diffraction data, and the deviation correction of its hardware equipment will effectively guarantee the correctness of the data. Therefore, the deviation type recognition should be carried out before correcting the deviation. In general, XRD equipment is calibrated by the the equipment manufacturer expert using the special instruments to inspect item by item. This calibration highly depends on personnel experience, and the process is complicated and unefficient. This paper discusses the XRD error types and causes, and develops a intelligent auxiliary correction system for XRD based on classification and prediction, to improve the efficiency of error recognition. The system uses the general computer hardware, analyzes the standard sample diffraction data, then identifies the instrument status and assists correction automatically by feature extraction, model training, and other steps. The results show that the system can quickly identify the deviation and achieve the purpose of auxiliary correction.