Transcriptome sequencing technology （RNA-seq） has been widely used in the field of basic scientific studies， but the bioinformatics analysis of RNA-seq data places high requirements on the programming ability of researchers.In order to enable researchers to analyze RNA-seq data simply and efficiently， this article constructed an automated and modular workflow-RNApipe （On Github：https：//github.com/ywu019/RNApipe.git） based on the Snakemake workflow management system and Conda environment manager.RNApipe can automatically conduct quality control， alignment， quantification， identification of differential genes， and functional annotation analyses including GO， KEGG， and GSEA with RNA-seq data from any species with a reference genome.The results of analysis in each step are presented in high-quality visualizations or reports， and important output files are preserved.RNApipe has been tested and evaluated in multiple model species.The results showed that RNApipe can run smoothly and the results of annotation are accurate.Compared with the existing pipelines of automated analysis， the main features of RNApipe include （i） the workflow is relatively complete， （ii） the default tools consume less time and resources， （iii） applicable to any parametric species， （iv） comprehensive visualization， and （v） user-friendliness （easy to install， use， and expand）.The features of RNApipe mentioned above allow researchers to quickly obtain essential information from large-scale RNA-seq sequencing data.