Abstract: A sort of incremental learning algorithm for support vector machine based on clustering was proposed. The nearest neighbor clustering algorithm was used for separating a whole training data set into several clusters, and each cluster subset was trained by support vector machine to obtain the support vector subset. The new sample data was firstly clustered in a certain subset. Then the distances between the new sample data and the support vectors of the cluster subset were calculated to weight every support vector. Finally, a new weighed model was formed with these samples. The proposed method was applied to a practical case of modeling prediction ability of the mechanical properties of steel materials. Comparing with the traditional support vector regression algorithm, this proposed method demonstrates its advantages of the smaller number of support vectors and the better generalization capability.