A trajectory big data mining method based on spatial-time Hausdorff distance segmentation and word vector similarity is designed in this paper. It can analyze the accompanying rules accurately and efficiently

and truly reflect the flow behavior of people and vehicles. The one-to-three Hausdorff distance algorithm based on time series characteristics can exclude the reverse trajectory and mine the accompanying relations. The set of trajectory segments separated by the time sliding window can establish the basis for the similarity measurement. The method of trajectory similarity measurement based on word vector establishes the analogical relationship between trajectory and sentences

reflects the spatial

temporal and directional heterogeneity of the trajectory

and accurately measures the structural similarity of the accompanying trajectories. It provides a reference for exploring similar objectives

detecting frequent paths as well as other related applications.