Similarity measurement of cloud model is a method that is used to measure the correlation between cloud models, which have same concept but different languages. Both similar cloud and its measurement analysis method are the extension of cloud model theory. To overcome the disadvantages of high consumption and low precision of calculation, a similarity measure algorithm based on overlap degree was proposed. Firstly, the position and logical relationships between these two clouds were defined according to three digital features: expected value, entropy and hyper entropy; secondly, the overlap degree of two clouds was calculated by using their location and shape features; finally, combined with overlap degree and similarity, the similarity measurement was converted to quantitative description of the overlapping part. In the time series classification experiments with compared Likeness comparing method based on Cloud Model (LICM), the computing consumption of the proposed measurement algorithm is reduced by 50% on the premise of ensuring the stability and accuracy. It is proved to be feasible and effective by the application.