Point-Based algorithms are a class of approximation methods for partially observable Markov decision processes (POMDP). They do backup operators on a belief set only, so linear programming is avoided and fewer intermediate variables are needed, and the bottleneck turns from selecting vectors to generating vectors. But when generate vectors, there will be a great deal of repeated and meaningless computing. This paper will propose a preprocessing method for point-based algorithms (PPBA). This method preprocesses each sampled belief point, and before generating α-vectors it estimates which action and α-vectors to be selected first, in so doing repeated computing is eliminated. Base-vector is also defined in this paper, which cancels meaningless computing with sparseness of problem. Experiments on Perseus show that, PPBA accelerates the performance greatly.