In data mining such as time series classification, the similarity performance based on category of different datasets are significantly different from each other. Therefore, a reasonable and effective similarity measure is crucial to data mining. The traditional methods such as Euclidean Distance (ED), cosine distance and Dynamic Time Warping (DTW) only focus on the similarity formula of the data themselves, but ignore the influence of the knowledge annotation contained in different datasets on the similarity measure. To solve this problem, a learning method of time series similarity measure based on Siamese Neural Network (SNN) was proposed. In the method, the neighborhood relationship between the data was learnt from the supervision information of sample tags, and an efficient distance measure between time series was established. The similarity measurement and confirmatory classification experiments were performed on UCR-provided time series datasets. Experimental results show that compared with ED/DTW-1NN(one Nearest Neighbors), the overall classification quality of SNN is improved significantly. The Dynamic Time Warping (DTW)-based 1NN calssification method outperforms the SNN-based 1NN classification method on some data, but SNN outperforms DTW in complexity and speed of similarity calculation during the classification. The results show that the proposed method can significantly improve the measurement efficiency of the classification of dataset similarity, and has good performance for high-dimensional and complex time-series data classification.