Abstract: An adaptive method of cleaning structural health monitoring (SHM) data based on local outlier factor is provided, including following steps: step 1: dividing SHM data to be analyzed into a series of data segments by using adaptive sliding window (ASW) technology; step 2: extracting time-domain statistical factors and frequency-domain statistical factors of each of the data segments to refine data information, thereby forming objects for study; step 3: calculating an outlier degree of each of the objects by using a weighted multiscale local outlier factor (WMLOF) based on feature factors; and step 4: detecting anomalies in SHM data by comparing a WMLOF value with a threshold value. The adaptive method can improve data quality effectively.