Haploid identification plays a key role in the field of maize-haploid breeding. To achieve mass and automated identification, Near-infrared Spectroscopy (NIRS) Analysis Technology is widely used. Its advantages include online monitoring, rapid analysis, easy operation, lossless process, cost-effectiveness, etc. At the beginning of the experiment, NIRS data of haploid and polyploidy maize seeds are cross collected via JDSU’s near-infrared spectrometer. To enhance validity, this experiment encompasses a testing set of data besides a training set. After pre-processing, experiment data is subsequently mapped in a higher-dimensional space to enhance its divisibility, and haploid feature is extracted. Then the experiment establishes identification models to predict whether maize seeds are haploid. It needs to point out that the experiment applies different feature extraction algorithms, thus different identification models are established accordingly. The experiment results show that the feature extraction algorithm of Kernel Locality Preserving Projection (KLPP) guarantees accurate recognition in a more stable way. Recognition rate of testing set and training set reaches up to 95.71% and 96.43%. The above experiment proves that NIRS data of maize seeds can be classified more effectively and accurately through non-linear transformation (Gaussian kernel transform in this experiment) and high-dimensional spatial mapping. The above process also maintains partial characteristics of NIRS data. Therefore, this paper may provide some new idea and method for Maize Haploid Identification technology.