Big data has brought tremendous challenges for the traditional computing model, because of its inherent characteristics such as large volume, high velocity, high variety, low-density value. On the one hand, the large volume and high velocity require the techniques of massive data computation and analysis; on the other hand, the high variety and low-density value make big data computing tasks highly depend on the complex cognitive reasoning technology. To overcome the coexistence challenges of massive data analysis and complex cognitive reasoning, human-machine collaboration based crowd computing is an effective way to solve the big data problem. In crowd computing, task assignment is one of the basic problems. However the current crowdsourcing platforms cannot support the active task assignment, which iteratively assigns tasks to appropriate workers based on the knowledge background or users. To address this problem, we propose an iterative theme-aware task assignment framework, and deploy it into existing crowdsourcing platforms. The framework includes two components. The first component is task modeling, which models the tasks as a graph where vertices are tasks and edges are task relationships. The second component is the iterative task assignment algorithm, which identifies the themes of the workers by their historical records, computes the workers’ accuracy on different themes, and assigns the tasks to the appropriate workers. Various experiments validate the effectiveness of our method.