Documents　contain　abundant　information　available　for　managerial　decision-making.　However,　manual　methods　of　screening　document　information　lack　accuracy　due　to　the　heterogeneity　of　documents.　To　address　the　above　issue,　we　propose　a　multimodal　network　combining　multivariate　semantic　association　graphs,　MMIE,　for　accurately　extracting　information　from　documents.　Firstly,　the　multivariate　semantic　graphs　between　multimodal　data　within　each　modality　are　constructed　based　on　the　semantic　association　of　text　contents,　followed　by　the　semantic　relationships　in　the　graphs　to　lead　the　fusion　and　embedding　of　the　extracted　multimodal　data　and　improve　the　feature　representation　capability.　Subsequently,　the　semantically　linked　multimodal　information　is　fed　into　the　newly　constructed　multimodal　self-attention　module　to　better　establish　inter-modal　associations.　Finally,　a　supervised　comparison　learning　loss　function　is　employed　to　reduce　further　the　information　loss　due　to　sample　imbalance.　The　experimental　results　on　three　real　datasets　show　that　the　proposed　model　can　extract　feature　information　of　different　modal　data　more　accurately,　and　the　F1　scores　reach　87.28%\documentclass[12pt]{minimal}　\usepackage{amsmath}　\usepackage{wasysym}　\usepackage{amsfonts}　\usepackage{amssymb}　\usepackage{amsbsy}　\usepackage{mathrsfs}　\usepackage{upgreek}　\setlength{\oddsidemargin}{-69pt}　\begin{document}$$\%$$\end{document},　82.53%\documentclass[12pt]{minimal}　\usepackage{amsmath}　\usepackage{wasysym}　\usepackage{amsfonts}　\usepackage{amssymb}　\usepackage{amsbsy}　\usepackage{mathrsfs}　\usepackage{upgreek}　\setlength{\oddsidemargin}{-69pt}　\begin{document}$$\%$$\end{document},　and　81.17%\documentclass[12pt]{minimal}　\usepackage{amsmath}　\usepackage{wasysym}　\usepackage{amsfonts}　\usepackage{amssymb}　\usepackage{amsbsy}　\usepackage{mathrsfs}　\usepackage{upgreek}　\setlength{\oddsidemargin}{-69pt}　\begin{document}$$\%$$\end{document}　,　respectively.