In light of the intricate, resource-intensive, and time-consuming characteristics associated with high-throughput experiments, the computational prediction of RNA-binding Protein (RBP) binding sites presents an efficacious strategy. Numerous methodologies have surfaced to forecast RNA-protein binding sites, typically necessitating the amalgamation of diverse features derived from annotated knowledge, including original RNA (Ribonucleic Acid) sequences and secondary structures. This integration serves to augment overall predictive performance. In this context, we propose an efficient and simple hybrid model, i.e., DeepCBA, which is only related to the original RNA sequences compared to other methods, thus simplifying the computational process. The model extracts local information and global context features with the help of multi-scale CNN convolution and BiLSTM (Bidrectional Long Short-Term Memory). Meanwhile, we make it possible to focus more on important features by introducing an attention mechanism in BiLSTM, which enables us to predict RNA-protein binding sites more efficiently. Empirical findings demonstrate that DeepCBA exhibits superior performance compared to contemporary methods in the realm of binding site prediction. This finding not only highlights the validity and performance advantages of the model, but also provides a powerful tool and method for future RNA-binding protein research. This means that DeepCBA is expected to be an important contribution to the field of RNA-binding protein research and to promote the further development of related fields.