With the popularization of intelligent hardware, wireless sensor networks have led to mobile crowdsensing (MCS) systems, which provide solutions for large-scale and complex urban data collection. Task distribution is the most important part of intelligent hardware applications. MCS can improve the task distribution efficiency by accurately predicting the location of a perceived user for task distribution. This paper proposes a task location estimator based on a variable-order Markov time window sensing (TEMTWS) algorithm. This method is based on time window modeling, and the association between user tasks is established by sensing the historical track data of user execution tasks. First, the task execution frequency and task vector are calculated, and the organizer at each position is selected. To obtain more perceptual users, similarity estimation is performed on the users and organizers within the time window, and users with high relevance are grouped into the same cluster. An experiment is conducted with the Gowalla dataset to verify the algorithm. The results show that the proposed algorithm outperforms the standard Markov K-means algorithm and K means-GA algorithm in terms of the prediction accuracy.