Loop　closure　detection　(LCD)　is　a　challenging　task　to　judge　whether　the　current　position　of　an　intelligent　robot　returns　to　the　previously　visited　position.　Mainstream　appearance-based　approaches　apply　robust　image　representation　techniques　to　describe　the　scene.　However,　most　of　these　methods　are　designed　for　single　images,　and　the　sequence　representation　method　incorporating　temporal　sequence　information　is　still　in　the　preliminary　exploration.　In　this　paper,　we　propose　a　compact　sequence　representation　method　for　hierarchical　LCD,　ensuring　conspicuous　performance　for　the　LCD　task.　Deriving　from　a　group　of　global　features　of　image　sequences,　we　propose　a　multi-scale　asymmetric　temporal　convolution　network　(MATC-Net),　which　generates　sequential　features　and　transformed　global　features　through　its　aggregation　branch　and　transformation　branch,　respectively.　Based　on　these　two　types　of　features,　a　MATC-Net-based　hierarchical　LCD　framework　including　two　similarity　measurement　processes　is　constructed,　through　which　the　best　place　match　is　identified.　The　experimental　results　show　that　our　method　outperforms　other　counterparts　on　three　datasets,　exhibiting　the　promising　potential　of　leveraging　sequential　features　to　LCD　task.