Near-infrared spectrum analysis as a rapidly developing technique has been applied in recognition analysis because of their simplicity, promptness and low cost. It was used to build an effective model to qualitatively analyze the corn. To evaluate the analysis results, an innovative grading evaluation index, defined with the relative distance of inter-species, was proposed for optimizing the near-infrared spectrum analysis process. It was applied to analyze the effect on optimizing the performance of the near-infrared spectrum qualitative analysis of corn. Firstly, two group spectral data were measured including the transmittance of 6 corn species sampled in Beijing (group A) and the reflectance of 6 corn species sampled in Hainan province (group B). The sampling data were processed involving original spectral data, the spectral data after pre-processing, and the spectral data after feature extraction from the group A and B experimental data. The relative distances of inter-species were calculated by using correlation, Euclidean distance, and entropy respectively. The result of contrast analysis showed that Euclidean distance was an effective calculation method for varieties recognition with good performance both in group A and B. Secondly, the reflectance of 6 corn species sampled in Henan province (group C) was measured. The Euclidean distance method was used to calculate the inter-specific relative distance between process steps as mentioned above. As a result, after the adjustment of the pretreatment algorithm, the relative distance between species increased from 0.6582 to 1.2972, and the correct recognition rate increased from 40.86% to 70.08%. By optimizing the feature extraction algorithm, the relative distance between species increased from 1.3102 to 2.4910, and the correct recognition rate increased from 68.32% to 93.27%. It was indicated that the correct recognition rate could be improved by the evaluation of the data analysis process.