The defect of coarse grains on the surface of cast-rolled aluminum slabs significantly impacts the yield of subsequent rolling processes. These coarse grains manifest not only in size variations but also in pronounced orientation differences. Therefore, investigating the orientation characteristics of surface grains in cast-rolled slabs is crucial for improving the microstructure and surface quality of cast-rolled products. Traditional inspection methods, which primarily rely on macroscopic observation after acid etching, struggle to quantitatively reflect grain orientation variations. To address the challenge of accurately extracting grain orientation information solely through optical characteristics—which are inherently unstable—due to the fine grain size and complex orientation of cast-rolled aluminum alloys after acid etching, this study proposes a method that integrates optical and height features to obtain grain orientation characteristics of cast-rolled aluminum slabs. This method first utilizes feature engineering to construct a multidimensional feature set—including grayscale co-occurrence matrices(GLCM) and geometric curvature—based on raw optical and height measurements of grain orientation and microstructure surfaces. This approach extracts additional etching micro-morphology information. Subsequently, post-processing trains and optimizes the prediction outputs of the constructed stacked ensemble model. The selected stacked model achieved an accuracy of 78.3% on the test set, with a macro-average F1-score of 0.784. In the validation experiments, the predicted results showed good consistency with the actual orientations in spatial distribution, achieving an accuracy rate of 80.1% , and successfully enabling the prediction of grain orientation features across larger regions. Finally, the SHAP (SHapley Additive exPlanation) interpretability model was employed to substantiate the scientific validity of the model. SHAP analysis results indicate that optical features and texture features within the microstructure of acid-etched cast-rolled aluminum effectively reflect grain orientation variations in the model. Height features primarily serve to regulate and stabilize the model. The combined action of these three elements collectively achieves the model's excellent predictive performance. This study provides a viable technical approach for obtaining grain orientation information from the surface microstructure of acid-etched cast-rolled aluminum alloys and similar alloys, and for efficiently achieving quantitative identification of crystallographic orientation across large areas.