Most Mobile Crowd Sensing （MCS） task allocation methods are specific to a single task and are difficult to apply to real-world scenarios of real-time concurrent multi-task. And it is often necessary for these methods to obtain user location in real time， which is not conducive to the protection of participant privacy. Concerning the above problems， a distributed multi-task allocation method for user area was proposed， named Crowd-Cluster. Firstly， the global perception task and the user area were clustered by using the greedy heuristic algorithm. Secondly， based on the spatial correlation， the Q-learning algorithm was used to combine the concurrent tasks into the task path. Then， the task path was dynamically priced by constructing user intention model that satisfying the Boltzmann distribution. Finally， based on the historical reputation records， the participants were greedily selected to implement task allocation. Experimental results on the real dataset mobility show that Crowd-Cluster can effectively reduce the total number of participants and the total movement distance of users， and can also reduce the impact of insufficient perception resources on task completion in the low population density scenarios.