Hyperspectral imaging technology provides an effective means for non-destructive detection of soluble solids content (SSC) in blueberries. However, multiple interference factors, including the diversity of fruit biological characteristics and differences during the data acquisition process, significantly affect the predictive performance of models. To address this challenge, this study proposes a differential enhancement and multi-scale feature fusion framework. This framework extracts gradient dynamic features and curvature characteristics of spectral curves through a multi-order differential feature enhancement module, achieving hierarchical fusion of static spectral and local dynamic features, thereby improving the model’s robustness against multiple interference factors. Simultaneously, by combining a multi-scale feature extraction network, it promotes deep fusion of information at different scales and enhances feature representation capabilities. Experimental results on 13 sets of blueberry datasets demonstrate that the proposed method significantly outperforms existing models in SSC prediction performance, validating its effectiveness and superiority.