Micro-expressions　often　reveal　more　genuine　emotions　but　are　challenging　to　recognize　due　to　their　brief　duration　and　subtle　amplitudes.　To　address　these　challenges,　this　paper　introduces　a　micro-expression　recognition　method　leveraging　regions　of　interest　(ROIs).　Firstly,　four　specific　ROIs　are　selected　based　on　an　analysis　of　the　optical　flow　and　relevant　action　units　activated　during　micro-expressions.　Secondly,　effective　feature　extraction　is　achieved　using　the　optical　flow　method.　Thirdly,　a　block　partition　module　is　integrated　into　a　convolutional　neural　network　to　reduce　computational　complexity,　thereby　enhancing　model　accuracy　and　generalization.　The　proposed　model　achieves　notable　performance,　with　accuracies　of　93.96%,　86.15%,　and　81.17%　for　three-class　recognition　on　the　CASME　II,　SAMM,　and　SMIC　datasets,　respectively.　For　five-class　recognition,　the　model　achieves　accuracies　of　81.63%　on　the　CASME　II　dataset　and　84.31%　on　the　SMIC　dataset.　Experimental　results　validate　the　effectiveness　of　using　ROIs　in　improving　micro-expression　recognition　accuracy.