The Dynamic Time Warping （DTW） algorithm measures the similarity between two time series by finding the best match between two time series. Aiming at the problem of excessive stretching and compression during time series matching due to noise existing in the sequence， a Noise robust Dynamic Time Warping （NoiseDTW） algorithm was proposed. Firstly， after introducing extra noise into the original signal， and the problem of one point aligning multiple points in sequence alignment was solved. Secondly， by finding an optimal matching path between two time series with multiple possible matching paths， the influence of randomness of noise on the time series similarity measure was reduced. Finally， the matching paths were mapped to the original sequence. Experimental results show that compared to Euclidean Distance （ED）， DTW， Sakoe-Chiba window DTW （Sakoe-Chiba DTW） and Weighted DTW （WDTW） algorithms， combined with K-Nearest Neighbors （KNN）， the proposed algorithm has the classification accuracy improved by 1 to 15 percentage points compared to the suboptimal algorithm on eight time series datasets， respectively， indicating that the proposed algorithm has good classification performance and is robust to noise.