Near-infrared spectroscopy (NIRS) technology has certain feasibility in identifying crop seed varieties, but if the storage time of the seeds to be tested is different, the accuracy of the identification model will be affected. In order to reduce the influence of storage time on the recognition model and improve the models prediction ability, NIRS technology and image processing technology are combined to extract spectral features related to physiological and biochemical indicators of varieties and apparent image features related to varieties. In order to extract the optimal spectral features, an improved backward interval partial least squares (IM_BiPLS) spectral interval selection algorithm is proposed. Aiming at the problem that it is difficult to determine the number of segments of BiPLS, the algorithm changes the number of segments within a certain range and takes the ratio of the correlation coefficient of the model established by the combination interval obtained by each segment number and the root mean square error of cross-validation as the evaluation index. When the index is maximum, the band combination corresponding to the segment number is the best. The competitive adaptive reweighting method (CARS) removes the uninformative and collinear variables in the selected band of IM_BiPLS and further optimises the spectral features. In order to extract the apparent image features related to varieties, firstly, the image segmentation algorithm based on maximum entropy and double region marking is used to remove the regions of interest and segment the single seed image. Then a single seeds morphological, texture and color features are extracted, and the statistical average features of all seeds in each image sample are calculated. Finally, CARS are used to optimize these features to complete image feature extraction. Taking 10 yellow maize varieties as the research object, NIRS data and corresponding images of 216 samples were collected. For spectral data, use IM_BiPLS algorithm selects the band combination with 736 variables from 1845 variables in the full spectrum and uses CARS to optimize 29 spectral variables further; For image data, 29 image features are extracted, and 11 image features are further optimized by CARS. Respectively using the spectral feature band extracted by IM_BiPLS, the preferred feature wavelength extracted by IM_BiPLS_CARS, the image feature(image), the image feature extracted by CARS(image_CARS), and the fusion between IM_BiPLS_CARS and image_CARS(compound) as the input and the corresponding category of the sample as the output to set up BP neural network models. The test results show that the performance of the compound BP model is the best, the training accuracy and verification accuracy are 100%, and the test accuracy is 97.7%. The experimental results demonstrate that the fusion of NIRS features with image features can effectively improve the accuracy of the recognition model and reduce the impact of storage time on the model. This provide a reference method for achieving the non-destructive, rapid and accurate recognition of corn seed varieties.