Accurate evaluation of soil quality is one of the prerequisites for ensuring breeding quality, which is of guiding significance for evaluating seed quality and precise fertilization. Soil composition content is an important indicator of soil quality assessment; spectral technology has been proven to detect soil composition quickly and greenly. However, due to the limitations of different spectral excitation principles, single-spectral technology cannot meet the needs of multiple soil composition content detection in breeding fields. This study used laser-induced breakdown spectroscopy (LIBS) and visible-near-infrared spectroscopy (VIS-NIR) combined with intelligent algorithms to analyze 288 soil samples collected from the breeding corn field of Ningxia Runfeng Seed Industry. The prediction models of metal elements and soil organic matter (SOM) content were established, and the spatial visualization distribution of metal elements and SOM content was realized. The specific research is: (1) Detection of metal elements in the maize breeding field. After collecting LIBS spectral data using a collinear double pulse LIBS system, air-PLS was used to correct the baseline of the spectral data and reduce the experimental error. The selected characteristic spectral lines of metal elements were searched in the standard atomic spectrum database of the National Institute of Standards and Technology (NIST). Combined with the LIBS spectrum of national standard soil samples and the true value of metal element content, a partial least squares regression (PLSR) model of four metal elements (Na, K, Mg, Mn) was established in national standard soil samples. Among them, the prediction effect of Mn content was the best, $R_{\text{p}}^2$ reached 0.813, RMSEP was 0.155 g·kg^-1; (2) Detection of SOM in maize breeding field. After collecting visible-near infrared spectral data, the spectral data were preprocessed by Savitzky-Golay Convolution Smoothing (SGCS), first derivative transformation, and Multivariate Scattering Correction (MSC), and the PLSR prediction model of SOM content was established to evaluate the three pretreatment methods. The PLSR model established after MSC pretreatment was the best. Subsequently, Monte Carlo cross-validation (MCCV) was used to eliminate the samples of abnormal SOM content. Competitive Adaptive Reweighed Sampling (CARS) and Successive Projections Algorithm (SPA) were used to select the characteristic wavelengths, and the PLSR prediction models of SOM content were established to evaluate the two algorithms. It was concluded that the prediction model's performance, established by the characteristic wavelengths selected by the CARS algorithm, was improved. And the characteristic wavelengths selected by the CARS algorithm and the true value of SOM content were combined to establish PLSR and back propagation neural network (BPNN) prediction models. The PLSR model had the best effect, with $R_{\text{p}}^2$ of 0.864, RMSEP of 0.612 g·kg^-1, and RPD_v of 2.733. (3) Spatial visualization distribution of metal elements and SOM content in maize breeding field. The PLSR model established by national standard soil samples was used to predict the content of four metal elements in the maize breeding field, and the spatial distribution map of predicted value content was established. Finally, the SOM content spatial distribution map of the real value, PLSR model predicted value, and BPNN model predicted value was established. The results show that LIBS technology and visible-near infrared spectroscopy quantitative analysis technology can detect the content of metal elements and SOM in the soil of the breeding field, which provides a reference value for the detection and spatial visualization distribution of soil component content.