To　address　the　challenges　of　the　poor　representation　capability　and　low　data　utilization　rate　of　end-to-end　speech　recognition　models　in　deep　learning,　this　study　proposes　an　end-to-end　speech　recognition　model　based　on　multi-scale　feature　fusion　and　multi-view　self-supervised　learning　(MM-ASR).　It　adopts　a　multi-task　learning　paradigm　for　training.　The　proposed　method　emphasizes　the　importance　of　inter-layer　information　within　shared　encoders,　aiming　to　enhance　the　model＇s　characterization　capability　via　the　multi-scale　feature　fusion　module.　Moreover,　we　apply　multi-view　self-supervised　learning　to　effectively　exploit　data　information.　Our　approach　is　rigorously　evaluated　on　the　Aishell-1　dataset　and　further　validated　its　effectiveness　on　the　English　corpus　WSJ.　The　experimental　results　demonstrate　a　noteworthy　4.6　%　\documentclass[12pt]{minimal}　\usepackage{amsmath}　\usepackage{wasysym}　\usepackage{amsfonts}　\usepackage{amssymb}　\usepackage{amsbsy}　\usepackage{mathrsfs}　\usepackage{upgreek}　\setlength{\oddsidemargin}{-69pt}　\begin{document}$$\%$$\end{document}　reduction　in　character　error　rate,　indicating　significantly　improved　speech　recognition　performance　.　These　findings　showcase　the　effectiveness　and　potential　of　our　proposed　MM-ASR　model　for　end-to-end　speech　recognition　tasks.