 This paper proposes a semi-supervised extreme learning machine, CELNK algorithm for gas classification tasks. It uses a weighted kernel to combine the supervised and unsupervised learning methods which improves the classification accuracy compared to existing algorithms. The experiments conducted on a real-world dataset demonstrate that the proposed CELNK algorithm outperforms other state-of-the-art algorithms in terms of classification accuracy. This article was authored by Wei Dang, Jiao Yangua, Mingzhe Lu, and others.