## 1 Introduction  
The functionality of biomolecules within a cell largely depends on their localization within specific cellular compartments [1]. In other words, the role of biomolecules in a cell is closely related to their specific locations, as this directly affects their interactions with other molecules, thereby executing specific biological functions. The subcellular localization of RNA is of significant importance in the regulation of cellular functions, disease occurrence,and treatment. In particular, messenger RNA (mRNA), as a crucial participant in the protein-coding process, not only directly influences protein synthesis through its intracellular localization but is also closely linked to various biological processes such as cell signaling, metabolic regulation, and cell cycle modulation [2]. Studies have shown that different types of RNA molecules can be enriched in their respective specific cellular compartments, forming functional RNA-protein complexes (RNPs) [3; 4]. These complexes play critical roles in regulating gene expression, maintaining cellular homeostasis, and responding to environmental changes [5]. Additionally, abnormal RNA localization is closely associated with the occurrence of various diseases. For instance, in several pathological conditions such as neurodegenerative diseases and cancer, incorrect RNA localization can lead to abnormal gene expression and protein synthesis disruption, thereby exacerbating disease progression [6; 7; 8; 9]. Therefore, in-depth research into the mechanisms of RNA subcellular localization not only helps to uncover the fundamental principles of cell biology but may also provide new insights and methods for disease prevention, diagnosis, and treatment. In recent years, the field of biology has successfully applied machine learning and deep learning technologies to solve numerous problems. In 2019, [PERSON] et al. developed the first deep neural network-based mRNA localization prediction model, RNATracker [10]. This model integrated cutting-edge technologies, such as Convolutional Neural Networks (CNN) [11], Long Short-Term Memory networks (LSTM) [12], and attention mechanisms, and provided a method for detecting candidate sequences by masking 100 nt sequences at a time to evaluate their impact on the prediction. In 2020, [PERSON] et al. utilized binomial distribution and one-way ANOVA to identify the optimal nonamer combinations for effectively representing mRNA sequences [13]. Based on these combinations, they developed an SVM-based prediction model to accurately identify mRNA subcellular localization. This study emphasized the statistical selection of sequence features, ensuring that the model was grounded in biologically meaningful representations of mRNA sequences. In the same year, [PERSON] et al. introduced a novel machine learning-based prediction model, mRNALoc [14], which utilized mRNA primary sequence information and SVM to predict its distribution across five subcellular locations. Their study also included the development of user-friendly software and a web server, enabling convenient access for researchers. This model provided an efficient and practical tool for subcellular localization prediction. [PERSON] et al. developed the SubLocEP prediction model [15], employing a two-layer ensemble prediction method to improve accuracy in predicting the subcellular locations of sequence samples. This method combined the outputs of multiple base classifiers, leveraging ensemble learning to enhance the model's robustness and prediction reliability. [PERSON] et al. developed mRNALocater [16], a prediction model leveraging advanced machine learning techniques to predict mRNA localization in subcellular compartments. This model provided enhanced precision and efficiency, focusing on improving the accuracy of localization predictions for diverse compartments like the nucleus, cytoplasm, mitochondria, and endoplasmic reticulum. [PERSON] et al.