In the operation and maintenance of high-end manufacturing enterprises， the spare parts demand occurs randomly， accompanied by a large number of zero demand periods. At the same time， the corresponding sparse parts demand data is of small scale and has intermittent and distribution with lump formation characteristics. Consequently， most of current time series forecasting methods are hard to effectively predict the demand trends. To solve this problem， a predictability evaluation and joint forecasting method for intermittent time series was proposed. Firstly， a new intermittent-similarity metric was proposed. In this metric， the frequency and positions of the "0" element occurring in the two sequences were counted， while the metrics such as maximal information coefficient and average demand interval were combined to evaluate the tendency information and fluctuation pattern of the sequences effectively and realize the quantification of the predictability of the intermittent time series. Then， based on this metric， an intermittent-similarity hierarchical clustering method was constructed to adaptively select the sequences with high similarity and strong predictability as well as eliminate extremely sparse and unpredictable sequences. Moreover， the structured information between the sequences was explored and utilized， a Multi-output Support Vector Regression （M-SVR） model was constructed， thereby achieving the joint prediction of intermittent time series with small-scale data. Finally， the experiments were conducted on two public datasets （UCI （University of California Irvine） gift retail dataset and Huawei computer accessory dataset） and a real-world spare parts after-sales dataset of a large manufacturing enterprise， respectively. The results show that compared with several representative time series forecasting methods， the proposed method can effectively exploit the predictability of various kinds of intermittent sequences and improve the prediction accuracy of intermittent time series with small-scale data. Therefore， the proposed method provides a new solution for the spare parts demand forecasting of manufacturing enterprises.