Abstract: This article optimizes the sensitivity and stability calibration method of an oil chromatography online device in a power enterprise in Shandong Province, and proposes a scheme based on a multivariate dynamic analysis model, adaptive calibration algorithm, and deep learning auxiliary module. The optimization results show that after implementation, the detection error of low concentration gases decreased from ±1.5% to ±0.4%, and the stability error decreased from ±2% to ±0.8%. In addition, combined with environmental perception compensation strategies, the stability error of the equipment remains within ±1% under temperature and humidity fluctuations. The research results indicate that this optimization method significantly improves the detection accuracy of oil chromatography online devices, and has good promotion significance for equipment calibration and optimization in the field of power equipment monitoring.