Regularized ranking algorithm based on kernels has recently gained much attention in machine learning theory, and pairwise learning is the generalization of ranking problem. In this paper, a kernel-based regularized pairwise learning algorithm with a quasiconvex loss function is provided, the error estimate is given by using the quasiconvex analysis theory, and an explicit learning rate is obtained. It is shown that the sample error is influenced by the parameters in the loss function. The experiments show that our method is more robust compared with the ranking algorithm with the least square loss function.