Cobalt is a global strategic mineral resource. Laterite-type cobalt deposits are large and shallow, and are important targets for cobalt exploration. The traditional cobalt exploration process often relies on indoor testing and analysis to determine the level of cobalt content in field outcrops and the degree of its mineralization. The cobalt elemental content testing method often involves a complex sample preparation process. It relies on large-scale high-precision instrumentation, which makes it difficult to meet the demand for rapid testing and exploration. Visible-near infrared and short-wave infrared (VNIR-SWIR) spectroscopy offers the advantages of portability, high efficiency, and non-destructiveness to the samples. It demonstrates excellent applicability to field sample testing scenarios. The measured spectral analysis of the samples shows that 620~810 nm reflects the absorption of cobalt ions and iron ions, 810~1 200 nm reflects the absorption characteristics of Fe²⁺, 1 350～1 450 and 1 850～2 040 nm reflect the absorption characteristics of —OH and H₂O, and 2 140～2 260 and 2 260～2 360 nm reflect the absorption characteristics of Al—OH and Mg—OH, respectively; accordingly, the sensitive wavelength range and the characteristic absorption characteristics of the samples are selected. Accordingly, the range of sensitive bands, the characteristic absorption peak parameters and the ratio of sensitive bands were selected as the spectral combination parameters, and the XGBoost (Extreme Gradient Boosting) regression algorithm was applied to establish the cobalt content quantitative inversion model; based on which, parameter optimization was carried out to obtain the optimal cobalt content quantitative analysis model, and the validation set had the values of R² 0.95, the RMSE was 89.19, the RPD was 4.35, and the model inversion accuracy was high. The histogram of feature importance shows that the sensitive band of cobalt element ranges from 620~810 nm, and the accuracy of the model is significantly improved after increasing the weights of absorption features of minerals closely related to cobalt content (chlorite, serpentine). The above results-demonstrate that the cobalt content of lateritic cobalt ore samples can be accurately estimated based on VNIR-SWIR spectra. The model, which incorporates combined spectral parameters, has the capability of rapidly determining cobalt content in field outcrops, offering significant application value for lateritic cobalt ore exploration.