Aiming at the problem that most trajectory similarity measurement algorithms cannot distinguish the trajectories with opposite directions， a three-dimensional Triangulation Division （3TD） algorithm based on three-dimensional space area division was proposed. Firstly， the absolute time series of the trajectory set was transformed into the relative time series according to the time conversion rules of the 3TD algorithm. Then， in the three-dimensional space coordinate system composed of three elements of longitude， latitude， and time， the area between trajectories were divided into several non-overlapping triangles by partitioning rules， and the areas of the triangles were accumulated and the trajectory similarity was calculated. Finally， the proposed algorithm was compared with the Longest Common SubSequence （LCSS） algorithm and Triangle Division （TD） algorithm on the randomly sampled trajectory dataset collected from the ship Automatic Identification System （AIS）. Experimental results show that the accuracy of the 3TD algorithm reaches 100%. At the same time， the proposed algorithm can also maintain accurate measurement results and high operation efficiency on massive datasets and datasets with partial missing trajectory points， which can better adapt to the similarity measurement of divergent trajectories.

In the era of big data， the application of spatial-temporal trajectory data is increasing and these data contain a large amount of information， and the similarity measurement of the trajectory plays a pivotal role as a key step in the trajectory mining work. However， the traditional trajectory similarity measurement methods have the disadvantages of high time complexity and inaccuracy caused by the determination based on the trajectory points. In order to solve these problems， a Triangle Division （TD） trajectory similarity measurement method with the trajectory area metric as theory was proposed for trajectories without road network structure. By setting up “pointer” to connect the trajectory points between two trajectories to construct the non-overlapping triangle areas， the areas were accumulated and the trajectory similarity was calculated to confirm the similarity between the trajectories based on the thresholds set in different application scenarios. Experimental results show that compared with the traditional trajectory point-based spatial trajectory similarity measurement methods such as Longest Common Subsequence （LCSS） and Fréchet distance metric， the proposed method improves the recognition accuracy， reduces the time complexity by nearly 90%， and can better adapt to the trajectory similarity measurement work with uneven distribution of trajectory points.