Knowledge　Base　Question　Answering(KBQA)　aims　to　interpret　natural　language　questions　and　obtain　answer　entities　via　retrieval　and　inference　in　a　Knowledge　Graph　(KG).　Recently,　considerable　attention　has　been　given　to　multi-hop　KBQA,　which　faces　a　significant,　yet　often　overlooked　challenge:complex　questions　typically　contain　multiple　relational　intentions　with　an　inferential　order　that　is　frequently　ignored.　To　address　this　challenge,　this　paper　proposes　a　multi-hop　KBQA　model　based　on　a　Hierarchical　Structure　Graph(HSG-KBQA).　This　model　is　designed　to　recognize　and　guide　the　relational　hierarchical　order　of　natural　language　questions,　enabling　the　selection　of　appropriate　relational　intents　at　each　inference　step.　The　proposed　hierarchical　structure　graph　explicitly　represents　the　relational　hierarchical　order　in　questions　and　encodes　the　dependency　information　into　the　question　using　an　LSTM-BiGCN　encoding　layer.　Furthermore,　the　concept　of　a　virtual　node　is　introduced　to　learn　the　state　of　the　KG　during　inference　utilizing　graph　pooling　techniques.　Additionally,　an　attention　mechanism-based　decoder　with　hierarchical　weights　is　presented,　aiming　to　optimize　instruction　generation.　This　ensures　that　each　hop　instruction　aligns　more　accurately　with　the　relational　chain　order　in　the　question.　Experimental　results　show　that　the　HSG-KBQA　model　demonstrates　superior　performance,　achieving　Hits@1　scores　of　71.3%　on　the　WebQuestionsSP　dataset　and　97.3%(PQ-2H)　and　89.7%(PQ-　3H)　on　the　PathQuestions　dataset.　These　results　are　superior　to　those　of　the　benchmark　model,　indicating　the　enhanced　capability　of　the　proposed　model　in　handling　the　KBQA　task.　©　2024,　Editorial　Office　of　Computer　Engineering.　All　rights　reserved.