When　the　dimension　of　text　data　is　high,　the　regularized　extreme　learning　machine　(ELM)　of　single　hidden　layer　structure　has　not　enough　ability　to　express　feature　in　the　text　classification.　To　solve　the　problem,　this　paper　presented　a　text　classification　method　based　on　multi-layer　extreme　learning　machine　(ML-ELM).　First,　the　method　used　the　compressed　representation　of　extreme　learning　machine-based　auto-encoder　(ELM-AE)　to　reduce　the　dimension　of　the　text　data.　Then,　the　structure　of　the　multi-hidden　was　used　to　represent　high-level　features　in　the　text　data,　and　the　method　of　least　squares　was　used　to　classify　the　text　data.　The　experimental　results　on　Reuters,　20newsgroup　and　Fudan　University　Chinese　Corpus　datasets　show　that　this　algorithm　has　a　good　classification　performance　compared　with　other　algorithms.　©　2019,　Editorial　Department　of　Journal　of　Beijing　University　of　Technology.　All　right　reserved.