The effective monitoring of hydrocarbon gas content is an important aspect of safety assurance in oil and gas exploration and production processes. Infrared spectroscopy, as a safe and efficient detection method, has attracted the attention of on-site engineers. However, it mainly uses offline models for measurement, which cannot cope with the complex working conditions and various nonlinear influencing factors on site, making it difficult for this non updated model to maintain high prediction accuracy. A weighted kernel partial least squares method based on fusion of similarity measurement criteria in just-in-time learning for quantitative analysis of alkane gases is proposed in this paper. Firstly, a similarity criterion based on fusion of multiple similarity measurement criteria is designed to effectively select historical samples for online modeling. Secondly, nonlinear kernel functions are introduced into local PLS models to effectively extract nonlinear features and compensate for the nonlinear processing ability of linear partial least squares models. The experimental results on the multi-component mixed gas infrared spectral data have verified the effectiveness of this method, with a goodness of R2 of 0.994 1. Compared with that of the PLS model, the RMSE and MRE of the proposed model have improved by 43.6% and 85.8%, respectively. It can be effectively used for online updating and high-precision prediction of infrared spectral quantitative analysis models for hydrocarbon gas.