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In the unlabeled sequence anomaly detection problem, the acquisition of data sequence features is not sufficient and cannot be effectively used, and the deep learning method is often used to detect the algorithm has poor interpretation. In order to solve the above problems, the helicopter flight data was taken as an example to study the anomaly detection of time series. Based on Iforest algorithm and PCA algorithm, a sliding window based sequence anomaly detection algorithm was proposed. By extracting the fluctuation and statistical information of the data through the sliding window, the sequence anomaly detection problem was transformed into a point anomaly detection problem. auc score was selected as the measurement standard to verify the improvement of the detection efficiency of the algorithm on multiple data sets with abnormal labels. Experiments were carried out on the unlabeled helicopter flight data set to verify the effectiveness of the algorithm. By comparing the changes of different characteristic variables in the detection process, the interpretability of the algorithm was illustrated from the algorithm level and the practical level.