Abstract: The occurrence and development themselves of loess have recorded abundant historical information, and the macro element content of loess can accurately reflect the environmental evolution. Hyperspectral remote sensing technology enjoys the advantages of being multi-band, continuous, and high-resolution. Therefore, it can be used to detect subtle differences in soil attributes and thus provide technical support for the fast and effective acquisition of basic loess information. In this paper, the loess profile of Zaoshugou Village, Zhengzhou City is studied. Combining the hyperspectral technology, the correlation between the spectral data and the macro elements of the loess was analyzed according to smoothed original spectra, first-order differential (FD), second-order differential (SD), de-envelope (CR), and reciprocal logarithm (Log(1/R). A partial least square regression (PLSR) model was established using the wave band with a larger correlation coefficient R as the characteristic band. The main conclusions are as follows. The variations in Ga, Fe, and Mg elements in the loess profile indicate that the study area has experienced a cold dry - warm wet - cold dry climate cycle since the Middle Holocene about 5400 aBP. The reflectance spectra of the loess in different stratigraphic units show the characteristics with similar trends. However, their spectral reflectance is in the order of L_0-2>L_0-1>L_t>S_0-1>T_S. According to the method of partial least squares, the optimal inversion models of Fe₂O₃, CaO, and CaO/MgO are the PLSR model with FD spectral transformation as the independent variable, while the best inversion model of MgO is the PLSR model with CR spectral transformation as the independent variable. The optimal inversion model of Fe₂O₃, CaO, and CaO/MgO can effectively distinguish different climate zones and indicate palaeoclimate cycle changes in the region where the study area falls. The optimal inversion model of MgO can better indicate the palaeoclimate evolution law of the region where the study area falls and thus has a certain reference value.