In the present paper, support vector machine (SVM) based on convex combination kernel function will be used for classification of THz pulse transmission spectra. Wavelet transform is used in data pre-processing. Peaks and valleys are regarded as location features of THz pulse transmission spectra, which are injected into maximum interval features of term frequency-inverse document frequency (TF-IDF). We can conclude weight of each sampling point from the information theory. The weight represents the possibility that sampling point becomes feature. According to the situation that different terahertz-transmission spectra are lack of obvious features, we composed a SVM classification model based on convex combination kernel function. Evaluation function should be used as an evaluation method for obtaining the parameters of optimal convex combination to achieve a better accuracy. When the optimal parameter of kenal founction was determined, we should compose the model for process of classification and prediction. Compared with the single kernel function, the method can be combined with transmission spectroscopic features with classification model iteratively. Thanks to the dimensional mapping process, outstanding margin of features can be gained for the samples of different terahertz transmission spectrum. We carried out experiments using different samples The results demonstrated that the new approach is on par or superior in terms of accuracy and much better in feature fusion than SVM with single kernel function.
