The limited number and high similarity of learning clauses lead to limited historical information and imbalanced search tree. In order to solve the problems, a dynamic branching strategy of award and punishment was proposed. Firstly, the variables of every unit propagation were punished. Different penalty functions were established according to whether the variable generated a conflict and the conflict interval. Then, in the learning phase, the positive variables for the conflict were found according to the learning clauses, and their activities were nonlinearly increased. Finally, the variable with the maximum activity was chosen as the new branching variable. On the basis of the glucose3.0 algorithm, an improved dynamic algorithm of award and punishment named Award and Punishment 7 (AP7) was completed. The experimental results show that, compared with the glucose3.0 algorithm, the rate of cutting branches of AP7 algorithm is improved by 14.2%-29.3%, and that of a few examples is improved up to 51%. The running time of the improved AP7 algorithm is shortened more than 7% compared with the glucose3.0 algorithm. The branching strategy can efficiently reduce the size of search tree, make the search tree more balanced and reduce the computation time.