Abstract: The traditional supervised learning is difficult to deal with real-world datasets with less labeled information when the training sets class is imbalanced. Therefore, a new semi-supervised Gaussian process classification of addressing was proposed. The semi-supervised Gaussian process was realized by calculating the posterior probability to obtain more accurate and credible labeled data, and embarking from self-training semi-supervised methods to add class label into the unlabeled data. The algorithm makes the distribution of training samples relatively balance, so the classifier can adaptively optimized to obtain better effect of classification. According to the experimental results, when the circumstances of training set are class imbalance and much lack of label information, The algorithm improves the accuracy by obtaining effective labeled in comparison with other related works and provides a new idea for addressing the class imbalance is demonstrated.