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In the actual business, parts demand occured randomly and demand fluctuates, so the demand sequence for spare parts showed obvious intermittent distribution. At the same time, due to factors such as manual reporting errors or special events, the actual demand for spare parts was prone to abnormal changes, making it difficult for traditional time series prediction methods to capture the evolution of the demand for accessories, resulting in high uncertainty and insufficient reliability of prediction results. To solve this problem, an adaptive interval prediction method for intermittent series based on tensor representation was proposed. Firstly, hierarchical clustering was used to screen similar sequences based on the average demand interval and square of the coefficient of variation of accessory sequences, forming sequence clusters to increase predictability. Secondly, the original demand sequence was reconstructed by tensor decomposition. The outliers in the sequence were then corrected while retaining the core information of the original sequence to maximum extent. Finally, an adaptive prediction interval algorithm was constructed, which could obtain the predicted value and prediction interval of the parts demand through the dynamic update mechanism to ensure the reliability of the results. The proposed method was validated on the aftersales dataset from a large vehicle manufacturing enterprise. Compared with existing time series prediction methods, the proposed method could effectively extract the evolutionary trend of various types of intermittent series and improve the prediction accuracy on the intermittent time series with small size as well. Experiments showed that the average root mean square scaled error (RMSSE) of this method was 0. 32 lower than that of the mainstream in-depth learning method of demand prediction. More importantly, when the prediction results were distorted, the proposed method could provide a reliable and flexible prediction interval, which could be helpful to provide a feasible solution for intelligent parts management. [ABSTRACT FROM AUTHOR]