Abstract: With the continuous development of in-line inspection, two or more rounds of in-line inspection have been performed to most pipelines, and a large number of in-line inspection data are obtained. Due to the influence of external environment and detection error, the in-line inspection data in multiple rounds have deviations in mileage, defect identification and quantification, they are difficult to be aligned rapidly, and manual alignment is a huge workload. In order to study the rapid alignment method of in-line inspection data, an in-line inspection data alignment algorithm model was established based on the massive in-line inspection data, and with the model, the rapid alignment of in-line inspection data was realized, for which application test was conducted with the in-line inspection data of different units in different formats. The test results show that: the achievable alignment with this method is 100% for the features of pipeline valve and tee, over 99% for pipe joint and at least 90% for elbow. Further, based on the method, the inspection data could be quickly mined and analyzed in depth and the development trend of defects on pipe body could be predicted, so that data support could be provided for internal and external corrosion control and management of pipe body, the risks of pipe body could be pre-controlled, and the pipeline integrity management could be improved.