The interaction between RNA and small molecules is crucial in various biological functions. Identifying molecules targeting RNA is essential for inhibitor design and RNA-related studies. However, traditional methods focus on learning RNA sequence and secondary structure features, neglecting molecule characteristics and resulting in poor performance on unknown molecule testing. To overcome this limitation, we developed a double-layer stacking-based machine learning model called ZHMol-RLinter. This approach more effectively predicts RNA-small molecule binding preferences by learning RNA and small molecule features to capture their interaction information. ZHMol-RLinter also combines sequence and secondary structural features with structural geometric and physicochemical environment information to capture the specificity of RNA spatial conformations in recognizing small molecules. Our results demonstrate that ZHMol-RLinter has a success rate of 90.8% on published testing set RL98, representing a significant improvement over existing methods. Additionally, ZHMol-RLinter achieved a success rate of 77.1% on the unknown molecule UNK96 testing set, showing substantial improvement over existing methods. The evaluation of predicted structures confirms that ZHMol-RLinter is reliable and accurate for predicting RNA-small molecule binding preferences, even for challenging unknown molecule testing. Predicting RNA-small molecule binding preferences can help us understand RNA-small molecule interactions and promote the design of RNA-related drugs for biological and medical applications.