We combined near-infrared spectroscopy with the deep learning theory to propose a method for rapidly detecting the content of each component in cotton-polyester blended fabric based on the Dropout deep belief network (DBN). Firstly, wavelet transform was used to compress the original spectral data. Then, a DBN model with a Gaussian restricted Boltzmann machine (GRBM) as the core was constructed, which could ensure the integrity of input data. Finally, Dropout was used to effectively prevent the model from overfitting and the interdependence between nodes was reduced by hiding some hidden layer nodes. As a result, network sparsification was achieved and the ability of nonlinear modeling and network model generalization was enhanced. The experimental results indicate that in the analytical model of the content of each component in cotton-polyester blended fabric built by the Dropout-DBN method, the correlation coefficients of prediction set for cotton and polyester contents are respectively 0.9927 and 0.9903, and the root mean square errors of prediction set are 0.0792 and 0.0869, respectively. The model proposed in this paper has much higher accuracy and adaptability than other modeling methods, which is conducive to model transfer and sharing and improves the intelligentization of the model.