To address the task scheduling problem of industrial software using Service-Oriented Architecture （SOA）， a task scheduling algorithm for SOA-based industrial software was proposed， considering the multiple attributes of tasks， the randomness， time-varying and coupling relationships of attributes， and the requirements of real-time scheduling and parallel processing of tasks. Firstly， the task scheduling problem was modeled， and a utility function was designed to evaluate the importance of the task. Then， Importance Ranking-based Scheduling Algorithm （IRSA） was proposed to schedule tasks in descending order of importance. Finally， a resource reservation mechanism and a preemptive scheduling mechanism were designed in IRSA to improve the efficiency of task scheduling. Experimental results show that compared with the four online scheduling algorithms such as First Come First Serve（FCFS）， Earliest Deadline First（EDF）， Least Laxity First（LLF）， and Fixed Priority Scheduling（FPS）， when the number of arrival tasks per second reaches 7.99， IRSA reduces the average response time of tasks by 55.83% to 61.27%， respectively， and has significant advantages on all performance metrics. Therefore， IRSA can achieve efficient task scheduling for SOA-based industrial software.

In order to solve the problem of class imbalance which the original degradation method causes in MIMLBoost algorithm, this paper introduced the importance of class into the original algorithm and an improved degradation method based on the category tag evaluating was proposed. First of all, the proposed method used a clustering algorithm to cluster all bags into groups. Each group could be treated as a concept in the multi-instance bag, and every class label could be quantified in each group. Then, the TF-IDF(Term Frequency-Inverse Document Frequency) algorithm was used to get the importance of each label in each group. Finally, for each group, the label whose importance was lowest in the group could be removed, because this label created many negative samples easily when the MIML (Multi-Instance Multi-Label) samples were transformed into multi-instance samples. The experimental results show that the new degradation method is effective, and the performance of improved algorithm is better than the original algorithm, especially in the terms of Hamming loss, coverage and ranking loss. This confirms that the new algorithm can reduce the error rate of classification and improve the precision of algorithm effectively.