The improved time adaptive supported vector machine performs well for a single concept drift problem in theoretical analysis and experimental studies, but cannot solve multiple concept drift problem. However, several parallel non-stationary datasets with inherent correlation are sometimes involved in many practical scenarios, whose respective classification models should take such cohesions into account. In this paper, the concept of shared supported vector chain is proposed to reflect the common vectors in each classification model for each nonstationary dataset and accordingly, a multi-task concept drift classification approach, called shared vector chain supported vector machines(SVC-SVM), is developed. Experimental results on synthetic datasets and gas sensor array drift dataset show the effectiveness of the proposed algorithm when solving several correlative concept drift problems together.