Kick is one of the most common accidents in drilling operations. If the kick monitoring and diagnosis are not timely，serious well control risks such as blowouts can be caused. Due to the massive monitoring data in drilling fields，directly using these data as the input of a kick diagnosis model will increase the complexity of the model and thus affect the accuracy of the model. Moreover，the labeling cost of kick samples is high in the process of constructing the diagnosis model. To solve these problems，this paper develops an intelligent diagnosis method for kick diagnosis，which is based on the kernel principal component analysis-semi-supervised extreme learning machine（KPCA-SSELM）. Firstly，KPCA is adopted to integrate the drilling parameters，and the principal components are extracted to reflect the core information of the original data. Then，the SSELM algorithm is employed for model training. Finally，the field drilling data is compared with the results of SSELM，KPCA-ELM，and other models to verify the effectiveness of the model. The results show that the proposed model based on KPCA-SSELM has a higher kick diagnosis rate and model generalization ability than other models. The semi-supervised learning method can make full use of the information contained in the unlabeled data to train the network when the number of labeled samples is relatively small，which can further improve the model performance and has a good application prospect.