Because the division and resource allocation of big data tasks lacks rationality in big data processing procedure, a scheduling method for big data tasks was proposed. First, in order to establish a reasonable management system of big data tasks and standardize the big data task processing flow, the scheduling theory was introduced to handle big data tasks. Then, based on the natures of big data tasks, the datasets were analyzed and handled, the decision table was introduced to perform attribute reduction, so as to reduce the data amount of big data analysis tasks and improve the big data analysis efficiency. Finally, the fuzzy comprehensive evaluation method was adopted, and the result of fuzzy comprehensive evaluation was used as the basis for task scheduling, thereby improving the rationality of task resource allocation. Experimental results on University of California Irvine (UCI) datasets show that the average prediction accuracy of the proposed scheduling algorithm is 7.42 percentage points higher than that of the Naive Bayes (NB) algorithm, 5.16 percentage points higher than that of the error Back Propagation (BP) algorithm, and 3.74 percentage points higher than that of the Root Mean Square Prop (RMSProp) algorithm. For datasets with a large number of features, the prediction accuracy of the proposed algorithm is significantly improved compared to those of other algorithms. Compared with Heterogeneous Critcal Path First Synthesis (HCPFS) algorithm and Heterogeneous Improved Priority List for Task Scheduling (HIPLTS) algorithm, the proposed algorithm has the average Scheduling Length Ratio (SLR) decreased by 12.14% and 4.56% respectively, and the average speedup ratio increased by 7.14% and 42.56% respectively, showing that the algorithm can effectively improve the efficiency of task scheduling in big data systems. Comprehensive analysis shows that the proposed algorithm performs well in prediction accuraing, and is efficient and reliable.