Study on identification of seed varieties is an important means of ensuring seed quality. The paper uses hyperspectral image technology and fusing image feature to identify different varieties of delinted cottonseeds. Hyperspectral image data (400～1000nm) of 4 types a total of 240 delinted cottonseeds samples were acquired. In addition, the spectral information and 12 morphological characteristics such as length width area, and circularity were extracted. Moreover, 11 effective wave-lengths(EWs) were to be selected by successive projection algorithm(SPA). And then 11 EWs of the calibration set were used as input to build a partial least quares discriminant analysis(PLS-DA), soft independent modeling of class analogy(SIMCA), K-nearest neighbor algorithm(KNN), principal component analysis was combined with linear discriminant analysis (PCA-LDA) and quadratic discriminant analysis (PCA-QDA) were used to build models. The results showed that the total identification rate of the PLS-DA model were 93% for the calibration set and 90% for the prediction set, respectively. When using image information modeling analysis, the overall recognition rate of the model is not high, which showed that the effect of classification is not good when only using morphological characteristics of hyperspectral images. Then, we fused the spectral and morphological information of the feature band as input, and established the data fusion model based on the analysis of PLS-DA, SIMCA, KNN, PCA-LDA and PCA-QDA. It suggested that the data fusion model showed better performance than the individual image model and spectral model, PLS-DA model had the best recognition effect, the overall recognition rate of calibration set and prediction set was 98% and 97% respectively. The experimental results indicated that fusing the spectral and image information of hyperspectral images could effectively improved discrimination accuracy for delinted cottonseeds at the case of a small amount of wavebands.