Aspect-based　sentiment　analysis　is　a　challenging　subtask　of　sentiment　analysis,　which　aims　to　identify　the　sentiment　polarities　of　the　given　aspect　terms　in　sentences.　Previous　studies　have　demonstrated　the　remarkable　progress　achieved　by　memory　networks.　However,　current　memory-network-based　models　cannot　fully　exploit　long-term　semantic　relationships　to　the　given　aspect　terms　in　sentences,　which　may　lead　to　the　loss　of　aspect　information.　In　this　paper,　we　propose　a　novel　memory　network　with　hierarchical　multi-head　attention　(MNHMA)　for　aspect-based　sentiment　analysis.　First,　we　introduce　a　semantic　information　extraction　strategy　based　on　the　rotational　unit　of　memory　to　acquire　long-term　semantic　information　in　context　and　build　memory　for　the　memory　network.　Second,　we　propose　a　hierarchical　multi-head　attention　mechanism　to　preserve　aspect　information　and　enable　MNHMA　to　focus　on　the　critical　context　words　to　the　given　aspect　terms　in　sentences.　Third,　we　employ　a　fully　connected　layer　in　each　attention　layer　of　the　hierarchical　multi-head　attention　layer　to　simulate　the　nonlinear　transformation　of　sentiments,　thereby　acquiring　a　comprehensive　context　representation　for　aspect-level　sentiment　classification.　Experimental　results　on　three　commonly　used　benchmark　datasets　demonstrate　that　our　MNHMA　model　outperforms　other　state-of-the-art　models　for　aspect-based　sentiment　analysis.