Abstract RNA modification, which is evolutionarily conserved, is crucial for modulating various biological functions and disease pathogenesis. High resolution transcriptome-wide mapping of RNA modifications has facilitated both data resources and computational prediction of RNA modification. While these prediction algorithms are promising, they are limited in interpretability or generalizability, or the capacity for discovering novel post-transcriptional regulations. Here, we present NetRNApan, a deep learning framework for RNA modification site prediction, motif discovery and trans-regulatory factor identification. Using m5U profiles generated by FICC-seq and miCLIP-seq technologies and single-base resolution m6A sites from multiple experiments as cases, we demonstrated the accuracy of NetRNApan with more efficient and interpretive feature representations. For m5U modification, we uncovered five representative clusters with consensus motifs that may be essential by decoding the informative characteristics detected by NetRNApan. Furthermore, NetRNApan revealed interesting trans-regulatory factors and provided a protein-binding perspective for investigating the function of RNA modifications. Specifically, we discovered 21 potential functional RNA-binding proteins (RBPs) whose binding sites were significantly linked to the extracted top-scoring motifs for m5U modification. Two examples are ANKHD1 and RBM4 with potential regulatory function of m5U modifications. Meanwhile, the analysis of convolution layer parameters within the model offers valuable insights into the regulation of m6A in humans. Collectively, NetRNApan demonstrated high accuracy, interpretability and generalizability for study of RNA modification and mRNA regulation. NetRNApan is freely available at https://github.com/bsml320/NetRNApan.