For the traditional trajectory similarity calculation method, the measurement effect is not good, and the similarity measurement is difficult to achieve good results when the time series data is excessively distorted . Based on the accuracy and real-time requirements of many practical applications, this article is based on the dynamic time warping , combined with the idea of trajectory translation and the idea of global variable constraints, and gives an improved dynamic time warping algorithm through algorithm optimization and parameter analysis. Numerical experiment results show that the improved algorithm has a recognition rate of 90% in the measurement of trajectory similarity, which is an increase of 41.25% compared with the classic algorithm, and the measurement accuracy is significantly improved. Furthermore, as a trajectory similarity measurement function combined with spectrum clustering algorithm, it is applied to trajectory data clustering analysis. Experimental results of simulated trajectory data shows that clustering analysis based on the improved algorithm can clearly distinguish trajectory clusters and the clustering effect is ideal.