In the imbalanced internet protocol television(IPTV) dataset, the traditional algorithm performs not well in terms of predicting the user′s complaint. For this problem, this paper combines traditional network parameters that influence the network quality of service (QoS) with MOS score that objectively reflects the quality of experience (QoE) to predict user′s complaint. And then we propose an improved algorithm based on the existing ODR-BSMOTE-SVM algorithm for the defects that the over-sampling algorithm will produce noise and there is not any optimization for kernel parameters. In the improved algorithm, under-sampling algorithm, over-sampling algorithm and data cleaning algorithm are firstly used to process the original imbalanced dataset. Then, through searching for the approximate optimal value by adaptive variable kernel parameters, the classification effect is ultimately improved. Experimental results show that the improved algorithm performs better than the traditional standard support vector machine (SVM) and the ODR-BSMOTE-SVM algorithm in predicting user′s complaint.