Aiming at the problems of few labeled samples and low quality of feature fusion in visible and near infrared dual-band scene classification, a dual-band scene classification method based on convolutional neural network (CNN) feature extraction and naive Bayes decision fusion is proposed in this paper. First, the CNN model based on pre training is used as the feature extractor of dual-band image to avoid the over fitting problem caused by few labeled samples. Second, the calculation speed of support vector machine and the classification performance of each band are improved by the dimensionality reduction of principal component analysis and feature normalization method. Finally, using the dual band posterior probability as the naive Bayes prior probability, a decision fusion model is constructed to achieve scene fusion classification. Experimental results on the public dataset show that compared with single-band classification and dual-band feature cascade fusion classification methods, the recognition rate of the method is significantly improved, reaching 94.3%; it is 6.4 percentage points higher than the best method based on traditional features. The recognition rate is similar to the CNN-based method, and the execution is simple and efficient.