According to the problem of infrared hyperspectral data identification and the characteristics of target spectrum, hyperspectral data are analyzed and processed through three steps, first to noise estimation by homogeneous area method, second to data dimension reduction and feature extraction by principal component analysis (PCA) and linear discriminant analysis (LDA) algorithms and third to data classification by spectral minimum distance matching algorithm. The characteristics of PCA, independent component analysis (ICA) and LDA algorithms are compared and analyzed to realize infrared spectrum identification of different targets. Compared results show that LDA algorithm has better effect on spectral data characteristic separation comparing with that of PCA and ICA algorithms.