Multi-hop　reading　comprehension　is　an　important　task　in　machine　reading　comprehension,　aiming　at　constructing　a　multi-hop　reasoning　chain　from　multiple　documents　to　answer　questions　with　requirement　of　combining　evidence　from　multiple　documents.　Graph　neural　networks　are　widely　applied　to　multi-hop　reading　comprehension　tasks.　However,　there　are　still　shortcomings　in　terms　of　insufficient　acquisition　of　context　mutual　information　for　the　multiple　document　reasoning　chain　and　the　introduction　of　noise　due　to　some　candidate　answers　being　mistakenly　judged　as　correct　answers　solely　based　on　their　similarity　to　the　question.　To　address　these　issues,　dual　view　contrastive　learning　networks　(DVCGN)　for　multi-hop　reading　comprehension　are　proposed.　Firstly,　a　heterogeneous　graph-based　node-level　contrastive　learning　method　is　employed.　Positive　and　negative　sample　pairs　are　generated　at　the　node　level,　and　both　node-level　and　feature-level　corruptions　are　introduced　to　the　heterogeneous　graph　to　construct　dual　views.　The　two　corrupted　views　are　updated　iteratively　through　a　graph　attention　network.　DVCGN　maximizes　the　similarity　of　node　representations　in　dual　views　to　learn　node　representations,　obtain　rich　contextual　semantic　information　and　accurately　model　the　current　node　representation　and　its　relationship　with　the　remaining　nodes　in　the　reasoning　chain.　Consequently,　multi-granularity　contextual　information　is　effectively　distinguished　from　interference　information　and　richer　mutual　information　is　constructed　for　the　reasoning　chain.　Furthermore,　a　question-guided　graph　node　pruning　method　is　proposed.　It　leverages　question　information　to　filter　answer　entity　nodes,　narrowing　down　the　range　of　candidate　answers　and　mitigating　noise　caused　by　similarity　expressions　in　evidence　sentences.　Finally,　experimental　results　on　HOTPOTQA　dataset　demonstrate　the　superior　performance　of　DVCGN.　©　2023　Journal　of　Pattern　Recognition　and　Artificial　Intelligence.　All　rights　reserved.