In order to realize the fast and nondestructive detection, the cashmere and wool near infrared spectroscopy database is created which includes the data of 228 groups of cashmere and wool from various districts, and it is applied to the qualitative detection of cashmere and wool. First the process of database creation in the cashmere and wool detection based on near infrared spectroscopy is introduced. Then on the base of the near-infrared spectroscopy original data preprocessing of cashmere and wool, the principal components of the data are analyzed, and 12 kinds of principal components are chosen. The detection model of cashmere and wool with radial basis function (RBF) fuzzy neural network is build. The comparative analysis experiments with PCA-MD modeling demonstrate that the method combining near infrared spectroscopy database, principal components analysis (PCA) and improved RBF fuzzy neural network is an effective and nondestructive detection method for cashmere and wool, and it can rapidly build high-accuracy detection models of cashmere and wool fiber.