At present, it is difficult to achieve cross domain sentiment classification in microblog data. Many feature-based and case-based methods have low classification accuracy and slow computing speed. To solve these problems, a model-based adaptive support vector machine (SVM) method is proposed, that is MASVM, which can directly apply existing models to new target domain data. Firstly, Based on the many-to-one SVM adaptive model, the classifier decision function trained by the source domain data set is modified, and a classifier adapted to the target domain is created. Then, we extend the multiple classifier adaptation framework, and learn the weight control of the basic classifier according to the allocation performance of the basic classifier in the smaller target domain tag set. Finally, the basic classifier trained by the general corpus is used to minimize the error of the emotional classification through domain adaptation. The experimental results show that the proposed method is better than the performance of the benchmark method and some similar methods in terms of accuracy and computational efficiency. In addition, the different proportions of the target domain label data will also affect the classification performance. As the performance of the proposed method is better than the basic model and domain adaptive method, it shows similar performance with the upper bound model in the domain, and has the advantage of computing speed, so it can be used as a fast microblog emotion classification method.