Aiming at the difficulty of constructing real tour routes from sparse refueling trajectories of self-driving tourists,a sparse trajectory clustering algorithm based on semantic representation was proposed to mine popular self-driving tour routes. Different from traditional trajectory clustering algorithms based on trajectory point matching,in this algorithm, the semantic relationships between different trajectory points were considered and the low-dimensional vector representation of the trajectory was learned. Firstly,the neural network language model was used to learn the distributed vector representation of the gas stations. Then,the average value of all the station vectors in each trajectory was taken as the vector representation of this trajectory. Finally,the classical k-means algorithm was used to cluster the trajectory vectors. The final visualization results show that the proposed algorithm mines two popular self-driving tour routes effectively.