The invention relates to a task scheduling method in the field of high-performance clusters. The task scheduling method comprises the steps: after analysis of each working node of a Hadoop in a job submitting stage, acquiring an actual stage weight according with a task, after processing of a geometric mean method, establishing a reference standard of the stage weight for left tasks of the job; in a task feedback stage, adopting the reference standard for the left tasks of the job, and estimating the task residual execution time by combining with progresses of sub-stages; in a job feedback stage, solving a geometric mean of stage weights of all tasks by using a staging manner, and establishing a job name-stage weight mapping record to be used as a reference of executing subsequent jobs on the node. According to the task scheduling method, self-learning and information feedback can be respectively carried out on the task of each job in a multi-job parallel executing environment to obtain more accurate stage weight estimation, the accuracy of pre-estimating the task residual execution time is improved, the hit rate of selecting dated tasks is increased, and the optimal utilization of cluster resources is promoted.