Abstract: In this paper, we present a semi-supervised algorithm to learn Gaussian process classifiers, which is combined with nonparametric semi-supervised kernels in the presence of unlabeled data. This algorithm mainly includes the following aspects: 1) The spectral decomposition of graph Laplacians is used to obtain kernel matrices incorporating labeled and unlabeled data; 2) The convex optimization method is employed to learn the optimal weights of kernel matrix eigenvectors, which construct the nonparametric semi-supervised kernels; 3) The proposed semi-supervised learning algorithm is obtained by incorporating semi-supervised kernels into Gaussian process model. The main characteristic of the proposed algorithm is that we employ the nonparametric semi-supervised kernels based on the entire dataset into the Gaussian process model, which has an explicit probabilistic interpretation, and can model the uncertainty among the data and solve the complex non-linear inference problems. The effectiveness of the proposed algorithm is demonstrated by the experimental results in comparison with other related works in the literature.