Abstract: The northeastern black soil region, recognized as a critical grain-producing area in China, has experienced a continuous decline in soil organic matter (SOM) content in recent years. The application of hyperspectral remote sensing technology for SOM content retrieval plays a crucial role in assessing the current status of black soil and implementing conservation measures. To address the low retrieval accuracy resulting from scale discrepancies in spatial and spectral characteristics of black soil, this study developed a remote sensing retrieval model with multi-scale feature enhancement. By constructing a multi-scale feature enhancement structure, spectral features were systematically extracted at different scales. Furthermore, skip connections were incorporated to effectively integrate initial spectral features with deep hierarchical features derived from convolutional networks, thereby strengthening the model's spectral representation capacity. When benchmarked against traditional methods such as partial least squares regression and random forest models, the proposed model demonstrates enhanced capability in capturing multi-scale black soil features and establishing spectral relationships. This advancement enables accurate SOM content retrieval under complex conditions, providing both theoretical significance and practical value for advancing intelligent remote sensing retrieval and sustainable conservation of black soil resources.