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Semiconductor gas sensors are widely applied in agriculture and industrial fields for its low price and high sensitivity. For the physical shortcomings of gas sensors such as cross-sensitivity and lack of the stability, it is difficult to get steady and accurate result. In this paper we present a new strategy to extract features from the response of a thermally modulated semiconductor gas sensor, combined with support vector machine (SVM) pattern recognition method for gas identification. A signal pre-processing method and wavelet decomposition transformation (DWT) were applied to extract features of a signal thermal modulated semiconductor gas sensor's response curves. Experiment result shows that the proposed method can perform well in discrimination of CO, H2 their mixtures than traditional neural network.