The　control　of　connected　autonomous　vehicles　(CAVs)　for　cooperative　sensing　and　driving　in　mixed　traffic　flows　is　critical　for　the　development　of　intelligent　transportation　systems.　However,　the　challenge　lies　in　effectively　representing　and　learning　from　the　interactions　between　heterogeneous　traffic　agents,　as　well　as　the　complex　traffic　states　they　produce.　To　address　this,　we　propose　the　Multi-View　Graph　Convolution　Network　Reinforcement　Learning　(MVGRL)　algorithm　for　the　decision-making　and　control　of　CAVs　in　mixed　traffic　scenarios.　First,　multi-view　graphs　are　constructed　based　on　multiple　logical　correlations　and　the　perception-communication　relationship　of　heterogeneous　vehicles　in　mixed　traffic.　This　enables　us　to　represent　the　dynamic　vehicle　networks　and　gain　insights　into　vehicle　behavior.　Then,　the　features　of　multi-view　vehicle　network　graphs　are　derived　by　multi-view　graph　convolutional　network　reinforcement　learning.　Finally,　the　lane　change　decisions　of　CAVs　are　obtained　based　on　complex　traffic　state　learning.　In　the　highway　scenario,　the　model　is　able　to　control　the　CAVs　to　complete　the　ramp　exit　task　and　maintain　an　overall　efficient　traffic　flow.　Various　test　scenarios　were　formed　by　adjusting　the　traffic　conditions　under　a　highway　off-ramp　scenario　for　comparison　experiments.　The　results　show　that　the　proposed　MVGRL　algorithm　model　outperforms　in　decision-making　and　robustness　than　the　baseline　models.