Abstract: To address the lack of objective, quantitative standards for guqin soundboard material selection, this study proposes a prediction method for vibration characteristics based on computer vision–derived texture features. One hundred standardized soundboard samples were selected to acquire high-resolution images and acoustic vibration signals. Visual texture features—including Gray-Level Co-occurrence Matrix (GLCM) metrics (e.g., contrast, homogeneity, entropy) and Gabor filter energy—were extracted, alongside acoustic parameters such as fundamental frequency and spectral centroid. A nonlinear mapping model was constructed using the Gradient Boosting Regression Tree (GBRT) algorithm, and feature importance was interpreted via the Shapley Additive exPlanations (SHAP) method. Results reveal a strong physical correlation between macroscopic wood texture and acoustic response: texture contrast exhibits a correlation coefficient of 0.88 with fundamental frequency. The model achieves a coefficient of determination (*R*²) of 0.932 for fundamental frequency prediction, with a root mean square error (RMSE) of 4.25 Hz. Corresponding *R*² values for spectral centroid and harmonic-to-noise ratio (HNR) are 0.662 and 0.845, respectively. SHAP-based feature analysis shows that macroscopic structural features—particularly those characterizing density gradient and texture direction—account for over 70% of model contribution, whereas color-related features show no statistically significant influence. This work empirically validates the traditional guqin-making principle of “judging sound by texture” (*yin wén xuan yin*) and establishes a quantitative, image-based framework for objective soundboard material evaluation.