The content of protein in milk powder is an important factor to determine the quality of milk powder. The value of protein in milk powder can be falsely increased by adulterating melamine, which endangers the health of consumers seriously. In this study, melamine in the milk powder was identified quickly by Fourier transform mid-infrared (FTIR) spectroscopy combined with pattern recognition method (model). The objective analysis of the infrared spectrum is achieved with the help of pattern recognition technology, which overcomes the limitation, complexity and subjectivity of identification through spectra comparison. Pure milk powder samples and adulterated milk powder samples with different mass concentrations (0.01‰~0.2%) of melamine were prepared respectively. After collecting the mid-infrared transmission spectrum data of the samples, the original data were firstly normalized and then comprehensively analyzed using multiple pattern recognition (classification) models including unsupervised (clustering) and supervised (discriminating) methods. Among them, traditional principal component analysis (PCA), distance discrimination (Euclidean distance and Pearson correlation coefficient) and non-negative matrix factorization (NMF) unsupervised pattern recognition methods can not accurately identify pure milk powder and melamine-containing milk powder samples. The recognition sensitivity and specificity of the supervised model of partial least squares discriminant analysis (PLS-DA) method were also low. Finally, linear discriminant analysis (LDA) and non-correlated linear discriminant analysis (ULDA) were used, the identification of melamine-containing milk powder was successfully achieved, and the sensitivity and specificity of recognition were both 100%. In particularly, the ULDA method maximizes the distance between the two types of samples, selects the feature variables containing the best classification information, and distinguishes the samples with only one discriminant vector. Furthermore, the ULDA method was used to select the important variables (characteristic wavelengths) of the infrared spectra. The relationship between the retained variables and recognition accuracy was investigated. The identification of pure milk powder and melamine-containing milk powder was realized with fewer variables. The recognition concentration of melamine in milk powder can be as low as 0.01‰. Therefore, it is proposed to identify a small amount of melamine in the milk powder with simple and rapid way based on MIR spectroscopy, which has advantages over traditional chemical analysis methods and provides an effective way for the adulteration recognition and quality control of milk powder. The method is expected to extend to apply in the identification of other foods.