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.