Rice is the most important cereal crop in China. To accurately realize the variety identification and appearance quality evaluation of geographically iconic rice is not only related to consumers interests but also to the reputation of retailers and manufacturers, which is a widespread concern. Firstly, to realize the integrated application of milled rice variety recognition and appearance quality detection, a multi-parameter detection system for milled rice variety and appearance quality was established. The system uses an NIR spectrometer with a diffuse reflectance accessory to collect the spectral information of rice flour, which can realize the classification of milled rice varieties based on NIR spectroscopy. Multi-parameter detection of milled rice appearance quality was realized based on the image method using the Complementary metal-oxide-semiconductor (CMOS) camera. The detection objects included cracks, length/width, chalkiness, broken grains and yellow grains. Based on the above system, this paper proposed a milled rice variety classification method based on spectral-image feature model fusion to improve the classification accuracy of milled rice varieties. In this method, the NIR spectral features and multi-image features were used as the input parameters, the milled rice variety number was used as the output parameters, and a variety classification fusion model was established based on the Partial least squares (PLS) method. In the modeling process of each fusion scheme, the variable projection importance analysis (VIP) method was used to achieve the optimal selection of the input parameters. Then the optimal fusion model was determined by comparing the classification accuracy of different fusion schemes. Finally, the multi-parameter detection experiment of milled rice appearance quality and the performance comparison experiment of different milled rice variety classification methods were carried out. Experimental results showed that the detection system established in this paper could realize the multi-parameter detection of milled rice appearance quality, including broken rice rate, length-width ratio, fissured rice rate, chalky rice rate, and yellow-colored rice rate, for which the detection accuracy range was 89.2%~97.0%. The proposed milled rice variety classification method based on the spectral-image feature model fusion could improve the classification accuracy of milled rice varieties. Compared with the NIRS method, which has a better effect than the traditional methods, the classification accuracy of Wuchang, Xiangshui, Yinshui, and Yuiguang rice varieties can be improved by 2.5%~7.5% using the new variety classification method.