Abstract Haploid breeding is a significant technology of maize breeding. Rapid and accurate haploid kernel identification method has great significance to accelerating the efficiency of haploid breeding. At present, detecting genetic markers on the embryo of kernels by machine vision and determining oil content of maize kernels by nuclear magnetic resonance (NMR) are widely used to automatically identify haploid maize kernel. However, the machine vision method can only identify the haploid through embryo side of kernels, and the NMR method cannot distinguish them when haploid and diploid have the overlap oil content. The study was aimed exploring a rapid and accurate method to identify haploid maize kernel using near-infrared hyperspectral imaging technology to overcome the limitations of current automated haploid identification and to achieve more accurate screening of haploid. In terms of two representative varieties of maize (Zhengdan 958 and Nongda 616), the study adopted spectral features of hyperspectral imaging to discuss the influence of embryonic orientation (embryo faces to or against light source) on haploid identification model. Meanwhile, the separability of embryo and non-embryo and identification accuracy of joint modeling of embryo and non-embryo were analyzed. The study showed that the greater difference between embryo and non-embryo of haploid and diploid, but hyperspectral imaging method could effectively distinguish haploid and diploid through embryo or non-embryo. At the same time, with the qualitative analysis method, two maize varieties could accurately distinguished haploid and diploid with overlapping oil content based on joint modeling. In this case, the test set of haploid and diploid achieved yielded higher correct acceptance rate (CAR) of 99% and the false acceptance rate (FAR) were both below 1%, with a high accuracy rate. The study showed that it is feasible to recognize maize haploid using hyperspectral imaging technology, which can provide a reference for the later haploid sorting systems.