Time series classification is an important task in time series data mining and has attracted significant attention in recent years. An important part of this problem is the similarity measurement between time series. Among many similarity measurement algorithms, dynamic time warping (DTW) is very effective, which has been widely used in many fields such as video, audio, handwriting recognition, and biological information processing. DTW is essentially a point-to-point matching algorithm under the boundary and time consistency constraints, which is able to provide the global optimal matching between two sequences. However, there is an obvious deficiency in this algorithm, that is, it does not necessarily achieve reasonable local matching between sequences. Specifically, the time points with completely different local structure information may be incorrectly matched by DTW algorithm. In order to solve this problem, an improved DTW algorithm based on local gradient and binary pattern (LGBDTW) is proposed. Although the proposed algorithm is essentially a dynamic time warping algorithm, it takes into account the local gradient and binary pattern values of sequence points to carry out similarity weighted measurement, effectively avoiding points matching with different local structures. In order to make a comprehensive comparison, the algorithm is adopted as the similarity measurement of the nearest neighbor classification algorithm, and tests it on multiple UCR time series datasets. Experimental results show that the proposed method can effectively improve the accuracy of time series classification. In addition, some examples are provided to verify the interpretability of the proposed algorithm.