Light absorption gas sensing technology has characteristics of massive parallelism, cross-sensitivity and extensive responsiveness which make it suitable for the sensing task of an electronic nose (e-nose). Therefore, an innovational e-nose system based on continuous wide spectral (CWS) gas sensing was proposed in this paper, named as optical e-nose. The mathematical model of the system was established and corresponding experimental platform was built. Then four kinds of air contaminations (NO2, SO2, the mixture of SO2 and NO2 (SO2&NO2), C6H6) were chosen to test the platform and corresponding spectra were collected to verify the feasibility of the proposed system. In addition, typical classifiers like correlation coefficient (CC), Euclidean distance to centroids (EDC), k-nearest neighbor (KNN), multilayer perception neural network (MLP), support vector machine (SVM) and least squares support vector machine (LSSVM) were chosen for classification analysis. Experimental results show that the mean classification accuracy of experimental data is >90% which confirmed the effectiveness of the optical e-nose.