To solve the problem of overlapping absorption peaks in the UV analysis of gas insulated switchgear (GIS), this paper presents a quantitative detection method for multi-component trace gases combining Gram's Angle Field (GAF) and an improved model of VGG16. To enhance the feature expression of the original spectral signal, GAF is used to convert one-dimensional UV spectral signals into sequential images. A large amount of feature information is included in these feature images. The GAF feature map is then fed into the modified VGG16 model for feature identification of trace gas concentrations. Finally, the models of a convolutional neural network (CNN), VGG16, and SDP_VGG16 are compared to the single and mixed gases of CS2, SO2, and H2S collected at various concentrations. From the findings of the validation and the area under curve (AUC), the proposed method can successfully detect CS2, SO2, and H2S trace gases produced by SF6 breakdown, and it is a tried-and-true feature extraction technique.