Translation is a crucial step in gene expression. Over the past decade, the development and application of Ribosome profiling (Ribo-seq) have significantly advanced our understanding of translational regulation in vivo. However, the analysis and visualization of Ribo-seq data remain challenging. Despite the availability of various analytical pipelines, improvements in comprehensiveness, accuracy, and user-friendliness are still necessary. In this study, we develop RiboParser/RiboShiny, a robust framework for analyzing and visualizing Ribo-seq data. Building on published methods, we optimize ribosome structure-based and start/stop-based models to improve the accuracy and stability of P-site detection, even in species with a high proportion of leaderless transcripts. Leveraging these improvements, RiboParser offers comprehensive analyses, including quality control, gene-level analysis, codon-level analysis, and the analysis of Ribo-seq variants. Meanwhile, RiboShiny provides a user-friendly and adaptable platform for data visualization, facilitating deeper insights into the translational landscape. Furthermore, the integration of standardized genome annotation renders our platform universally applicable to various organisms with sequenced genomes. This framework has the potential to significantly improve the precision and efficiency of Ribo-seq data interpretation, thereby deepening our understanding of translational regulation.