A lot of Pull Requests (PRs) appear in Github everyday, and thus it is a very important work to review these PRs quickly in Github. Labeling PRs according to the PRs classification can improve the success rate and the review efficiency. However, recent research works have shown that most of the PRs are not labeled, and if the PR is labeled, it is done manually. To solve this problem, we propose a supervised classification model combined with supervised topics model and Naive Bayes classifier, which can make the PR be classified automatically. The method creates a one-one relationship between labels and PRs, and the approach classifies most PRs automatically with the only label which record the closest topic of PRs. The experimental results show that the proposed model can reach a precision of 60% in majority situation. The proposed model may support a better result via adjusting the parameters in case.