Math　word　problem　(MWP)　represents　a　critical　research　area　within　reading　comprehension,　where　accurate　comprehension　of　math　problem　text　is　crucial　for　generating　math　expressions.　However,　current　approaches　still　grapple　with　unresolved　challenges　in　grasping　the　sensitivity　of　math　problem　text　and　delineating　distinct　roles　across　various　clause　types,　and　enhancing　numerical　representation.　To　address　these　challenges,　this　paper　proposes　a　Numerical　Magnitude　Aware　Multi-Channel　Hierarchical　Encoding　Network　(NMA-MHEA)　for　math　expression　generation.　Firstly,　NMA-MHEA　implements　a　multi-channel　hierarchical　context　encoding　module　to　learn　context　representations　at　three　different　channels:　intra-clause　channel,　inter-clause　channel,　and　context-question　interaction　channel.　NMA-MHEA　constructs　hierarchical　constituent-dependency　graphs　for　different　levels　of　sentences　and　employs　a　Hierarchical　Graph　Attention　Neural　Network　(HGAT)　to　learn　syntactic　and　semantic　information　within　these　graphs　at　the　intra-clause　and　inter-clause　channels.　NMA-MHEA　then　refines　context　clauses　using　question　information　at　the　context-question　interaction　channel.　Secondly,　NMA-MHEA　designs　a　number　encoding　module　to　enhance　the　relative　magnitude　information　among　numerical　values　and　type　information　of　numerical　values.　Experimental　results　on　two　public　benchmark　datasets　demonstrate　that　NMA-MHEA　outperforms　other　state-of-the-art　models.　©　The　Author(s),　under　exclusive　licence　to　Springer-Verlag　London　Ltd.,　part　of　Springer　Nature　2024.