Aiming at the problem that the existing crowd-sourcing system can not effectively control the active enthusiasm of the workers and the quality of task completion in the process of crowd-sourcing interaction, a worker reputation model based on active degree was proposed to realize the quality control of the crowd-sourcing platforms. The model improved the average reputation model, and the concepts of active factor and historical factor were put forward from the point of view of workers' active degree and historical reputation value. First, the active factor of the worker was calculated according to his participating days in the crowd in the last 30 days, and then the historical reputation value of the crowd-sourcing worker was calculated according to the historical factor. Finally, the reputation value of the crowd-sourcing worker based on active degree was calculated based on the calculated active factor and historical reputation value, which was used to measure the ability of the crowdsourcing worker. The theoretical analysis and test results showed that compared with the average reputation model, the task completion quality of crowdsourcing workers selected by the worker reputation model based on active degree was increased by 4.95% and the completion time was decreased by 25.33%; compared with the trust model based on evidence theory, the task completion quality was increased by 6.63% and the completion time was decreased by 25.11%. The experimental results show that the worker reputation model based on active degree can effectively improve the quality of crowdsourcing tasks and reduce the completion time.