The　future　trajectory　prediction　of　heterogeneous　traffic-agents　for　autonomous　vehicles　in　mixed　traffic　scene　is　of　great　significance　for　safe　and　reliable　driving.　Thus,　we　propose　the　Multi-View　Adaptive　Hierarchical　Spatial　Graph　Convolution　Network　(MVHGN)　to　predict　the　future　trajectories　of　heterogeneous　traffic-agents.　Firstly,　multiple　logical　correlations　are　obtained　based　on　the　time　series　data　of　traffic-agents　and　a　multi-view　logical　network　is　constructed.　The　multi-view　logical　feature　extraction　is　realized　based　on　the　graph　convolution　module.　Then,　combining　the　multi-view　logical　features　and　the　adaptive　spatial　topology　network,　the　logical-physical　features　at　the　micro　level　are　mined　through　the　graph　convolution　module;　based　on　the　logical-physical　features　at　the　micro　level　and　the　regional　clustering　network　at　the　macro　level,　the　global　logical-physical　features　are　obtained.　Finally,　the　model　predicts　the　future　trajectories　of　traffic-agents　based　on　the　encoder-decoder　structure　of　the　GRU.　For　the　Apolloscape　trajectory　data　set,　the　performance　of　our　proposed　method　MVHGN　is　better　than　that　of　the　comparison　models.