Deep　neural　networks　have　made　significant　achievements　in　representation　learning　of　traditionally　man-made　features,　especially　in　terms　of　complex　objects.　Over　the　decades,　this　learning　process　has　attracted　thousands　of　researchers　and　has　been　widely　used　in　the　speech,　visual,　and　text　recognition　fields.　One　deep　network　multi-layer　extreme　learning　machine　(ML-ELM)　achieves　a　good　performance　in　representation　learning　while　inheriting　the　advantages　of　faster　learning　and　the　approximating　capability　of　the　extreme　learning　machine　(ELM).　However,　as　with　most　deep　networks,　the　ML-ELM＇s　algorithmic　performance　largely　depends　on　the　probability　distribution　of　the　training　data.　In　this　paper,　we　propose　an　improved　ML-ELM　made　via　using　the　local　significant　regions　at　the　input　end　to　enhance　the　contributions　of　these　regions　according　to　the　idea　of　the　selective　attention　mechanism.　To　avoid　involving　and　exploring　the　complex　principle　of　the　attention　system　and　to　focus　on　the　clarification　of　our　local　regional　enhancement　idea,　the　paper　only　selects　two　typical　attention　regions.　One　is　the　geometric　central　region,　which　is　normally　the　important　region　to　attract　human　attention　due　to　the　focal　attention　mechanism.　The　other　is　the　task-driven　interest　region,　with　facial　recognition　as　an　example.　The　comprehensive　experiments　are　done　on　the　three　public　datasets　of　MNIST,　NORB,　and　ORL.　The　comparison　experiment　results　demonstrate　that　our　proposed　region-enhanced　ML-ELM　(RE-ML-ELM)　achieves　performance　increases　in　important　feature　learning　by　utilizing　the　apriori　knowledge　of　attention　and　has　a　higher　recognition　rate　than　that　of　the　normal　ML-ELM　and　the　basic　ELM.　Moreover,　it　benefits　from　the　non-iterative　parameter　training　method　of　other　ELMs,　and　our　proposed　algorithm　outperforms　most　state-of-the-art　deep　networks　such　as　deep　belief　network(DBN),　in　the　aspects　of　training　efficiency.　Furthermore,　because　of　the　deep　structure　with　fewer　hidden　nodes　at　each　layer,　our　proposed　RE-ML-ELM　achieves　a　comparable　training　efficiency　to　that　of　the　ML-ELM　but　has　a　higher　training　speed　with　the　basic　ELM,　which　is　normally　the　width　single　network　that　has　more　hidden　nodes　to　obtain　the　similar　recognition　accuracy　with　the　deep　networks.　Based　on　our　idea　of　combining　the　apriori　knowledge　of　the　human　selective　attention　system　with　the　data　learning,　our　proposed　region-enhanced　ML-ELM　increases　the　image　classification　performance.　We　believe　that　the　idea　of　intentionally　combining　psychological　knowledge　with　the　most　algorithms　based　on　data-driven　learning　has　the　potential　to　improve　their　cognitive　computing　ability.