With the widespread use of GPS-enabled smart devices and the increased availability of wireless networks, mobile crowdsourcing system (MCS) has recently been proposed as a framework that automatically requests workers to perform location sensitive tasks. In the task allocation problem of MCS, existing algorithms lack consideration of the impact of nonadjacent and discontinuous execution of tasks on task allocation. Workers may have different task preferences in different time periods. Nonadjacent and discontinuous tasks provide important correlations for understanding workers’ behavior. This article introduces a novel task allocation algorithm based on spatiotemporal attention network (STATA). STATA takes into account factors such as the spatiotemporal distribution of tasks and workers as well as the location preferences and abilities of workers, and integrates them into a unified network for modeling. First, all historical tasks performed by workers are aggregated to obtain the correlation of all historical tasks. Then, the most plausible candidate tasks are recalled from the weighted representation for allocation. STATA utilizes the spatiotemporal attention mechanism to capture the relationship between these factors, ultimately improving the accuracy of task allocation. Extensive experiments demonstrate that the STATA model exhibits superior performance in terms of task allocation accuracy and practical application capabilities.