Aiming at the problems of the fixed task progress proportions and passive selection of slow tasks in the task speculation execution algorithm for heterogeneous cluster, an adaptive task scheduling algorithm based on the computation capacity difference between node sets was proposed. The computation capacity difference between node sets was quantified to schedule tasks by fast and slow node sets, and dynamic feedback of nodes and tasks speed were calculated to update slow node sets timely to improve the resource utilization rate and task parallelism. Within two node sets, task progress proportions were adjusted dynamically to improve the accuracy of slow tasks identification, and the fast node which executed backup tasks dynamically for slow tasks by substitute execution implementation was selected to improve the task execution efficiency. The experimental results showed that, compared with the Longest Approximate Time to End (LATE) algorithm, the proposed algorithm reduced the running time by 5.21%, 20.51% and 23.86% respectively in short job set, mixed-type job set and mixed-type job set with node performance degradation, and reduced the number of initiated backup tasks significantly. The proposed algorithm can make the task adapt to the node difference, and improves the overall job execution efficiency effectively with reducing slow backup tasks.