Due to the severity and great harm of coal and gas outbursts accidents, outbursts prediction becomes very necessary, the paper presents a hybrid prediction model of feature extraction and pattern classification for coal and gas outbursts. First, Discrete wavelet transform (DWT) is utilized as a processing technique to decompose subseries and extract the features with different frequency, and the optimal feature components are retained; Second, in order to eliminate the redundancy between the features and uncorrelation between feature and outbursts, we use the fast independent component analysis(FICA) feature extraction method based on high-order statistics to obtain each independent feature, obtaining the global information in the feature; then the obtained features are input into linear discriminant analysis(LDA) , under the guidance of class label, then the local information in features are obtained; Finally, the projected features are input into the deep extreme learning machine(DELM) classifier based on the optimal parameters by quantum particle swarm optimization(QPSO) for training and classification. The experimental results on the data set of coal and gas outbursts show that compared with other models in the current prediction of coal and gas outbursts, this method has significant effect on various indicators such as speed and recognition effect.