Since traditional Dynamic Time Warping (DTW) measurement method is prone to over-bending and has the shortcoming of high computation complexity and low efficiency, an Updated Dynamic Time Warping (UDTW) measurement method based on path correction was proposed. Firstly, the characteristic information of time series was extracted by Piecewise Local Max-smoothing (PLM) method (a dimensionality reduction method), so that the computational cost of UDTW was reduced. Secondly, considering the sequence similarity requirements of morphological characteristics, a dynamic penalty factor was set to correct the bending degree of the excessive bending path. Finally, based on updated distance metric, 1-nearest neighbor classification algorithm was used to classify time series data, which improved the accuracy and efficiency of the time series similarity measurement. The experimental results show that UDTW measurement method outperforms the traditional DTW measurement method among 15 UCR datasets, and the accuracy rate achieved 100% in 3 of them. In the comparison experiments with Derivative DTW (DDTW) measurement method, UDTW increases the classification accuracy by 71.8% at most, and the execution time of PLM-UDTW is reduced by 99% without decreasing classification accuracy.