Due to the enormous market value of geographically iconic,adulteration or fraud often occurs. Therefore,to ensure the brand benefits from geographically iconic rice and consumer rights,it is important to identify polished rice varieties accurately. Near-infrared spectroscopy is a common method to distinguish polished rice varieties. The varieties can be classified by extracting the different features of different types in near-infrared spectroscopy. However,there are some problems in the existing studies,such as insufficient characteristic wavelength selection performance and insufficient discrimination accuracy for specific varieties,which limit the improvement of the discrimination accuracy of polished rice varieties based on the near-infrared spectroscopy method. In response to the above problems,this paper studies the optimisation of milled rice variety identification based on near-infrared spectroscopy from the two aspects of characteristic wavelength selection and variety identification strategies for four types of rice,Wuchang,Xiangshui,Koshihikari,and Yinshui in Northeast China. First,permutation entropy (PE) and adaptive sliding window (ASW) were combined to improve the feature wavelength selection performance. An adaptive sliding permutation entropy (ASW-PE) based method for selecting the characteristic wavelength of polished rice spectrum was proposed and compared with the traditional algorithm. Secondly,a discriminant strategy based on the discriminant objective was proposed to improve the discriminant accuracy of different specified varieties. By studying the matching optimisation of the spectral preprocessing algorithm and classification modelling algorithm,a discriminant process of “specified cultivation-selected model-selected algorithm” was established. Experimental results show that using the adaptive sliding permutation entropy algorithm proposed in this article to select characteristic wavelengths can reduce the milled rice variety discrimination error by at least 50% compared with the traditional algorithm; using the milled rice variety judge strategy based on the discrimination target proposed in this article. Compared with the conventional judge strategy based on fixed models,the discrimination accuracy can be improved by at least 2.5%.