In order to reduce the storage cost and improve the learning efficiency when data arrives in batches in practical applications, an SVR-based incremental learning algorithm based on belief revision is proposed. In this algorithm, belief sets are extracted from the historical samples firstly, and appropriate belief sets are selected to establish the prediction SVR model according to the characteristics of belief sets and the new incremental data set. Then, the belief sets are revised and the current epistemic state is adjusted to adapt the new environment. The algorithm perform well when applied to online learning and incremental learning. Experiments conducted on the standard data sets and the airport noise data set demonstrate that the proposed algorithm is effective and superior to the traditional methods and the general incremental learning algorithms.