High quality cotton seeds are the basis of precision seeding technique. In this paper, near-infrared hyperspectral imaging technology is used to realize the visible identification of micro-damaged cotton seeds, which lays a theoretical foundation for the development of cotton seeds selection equipment. Near-infrared hyperspectral images of two kinds of 540 cotton seeds, undamaged and micro-damaged, were acquired, of which 405 samples were used as the calibration set, and 135 samples were used as prediction set. After analyzing the original spectral curve of the full wave band, the noise at both ends was removed. Firstly, KS algorithm was used to divide samples, and the spectra was pretreated by smoothing algorithm( Savitsky-Golay), respectively using the second derivative spectra (2nd spectra) method, principal component analysis loading (PCA-loading) method and successive projection algorithm (SPA) method to extract the feature wavelength, then partial least squares discriminant analysis (PLS-DA) model, K nearest neighbor (KNN) model and support vector machine (SVM) model ware used to analyze the characteristic spectrum. By comparing the analysis results, SPA-PLS-DA was selected as the model, the discrimination rate of the calibration set and the prediction set is up to 91.50% and 90.33%, respectively. Finally, the SPA-PLS-DA model is used to identify the mixed images of undamaged and micro-damaged cotton seeds. The identification results were identified by different colors,the corresponding visual identification figure is generated, and good recognition results were obtained. Moreover, the recognition rate of micro-damaged cotton seeds was above 90%. The result indicates that the near-infrared hyperspectral technology and image processing technology can be used to realize the visual identification of the micro-damaged cottonseeds.