the probability-based covering algorithm (pbca) is a new algorithm based on probability distribution. it decides, by voting, the class of the tested samples on the border of the coverage area, based on the probability of training samples. when using the original covering algorithm (ca), many tested samples that are located on the border of the coverage cannot be classified by the spherical neighborhood gained. the network structure of pbca is a mixed structure composed of both a feed-forward network and a feedback network. by using this method of adding some heterogeneous samples and enlarging the coverage radius, it is possible to decrease the number of rejected samples and improve the rate of recognition accuracy. relevant computer experiments indicate that the algorithm improves the study precision and achieves reasonably good results in text classification.