The concentration and types of SF<sub>6</sub> in Gas Insulated Switchgear (GIS) play a decisive role in the devices’ insulating property. A quantitative analysis of SF<sub>6</sub> and its decompositions can help to find the reason of fault. In order to find the concentration information of some special ramifications of SF<sub>6</sub> from the infrared spectrum of GIS’s gas, this paper proposes Particle Swarm Optimization combines with Support Vector Machine to analysis the insulating medium SF<sub>6</sub> and its ramifications quantitatively. This paper studies the spectrum of several ingredients that are mordant to the insulator instruments in the ramifications, such as HF and SO<sub>2.</sub> The mixed spectrum is divided into 13 parts, and the area of every part is calculated. The centre of each part is the characteristic peaks, and contains 35 wave numbers both side. These areas are used as the inputs of Support Vector Machine; the outputs is volumes of the three gases. The Particle Swarm Optimization is used to train the Support Vector Machine. The experiment shows Support Vector Machine based on Particle Swarm Optimization is time saved and accurate, which has practical significance and application potentiality.