The　application　of　Deep　Reinforcement　Learning　(DRL)　has　significantly　impacted　the　development　of　autonomous　driving　technology　in　the　field　of　intelligent　transportation.　However,　in　mixed　traffic　scenarios　involving　both　human-driven　vehicles　(HDVs)　and　connected　and　autonomous　vehicles　(CAVs),　challenges　arise,　particularly　concerning　information　sharing　and　collaborative　control　among　multiple　intelligent　agents　using　DRL.　To　address　this　issue,　we　propose　a　novel　framework,　namely　Spatial-Temporal　Deep　Reinforcement　Learning　(ST-DRL),　that　enables　collaborative　control　among　multiple　CAVs　in　mixed　traffic　scenarios.　Initially,　the　traffic　states　involving　multiple　agents　are　constructed　as　graph-formatted　data,　which　is　then　sequential　created　to　represent　continuous　time　intervals.　With　the　data　representation,　interactive　behaviors　and　dynamic　characteristics　among　multiple　intelligent　agents　are　implicitly　captured.　Subsequently,　to　better　represent　the　spatial　relationships　between　vehicles,　a　graph　enabling　network　is　utilize　to　encode　the　vehicle　states,　which　can　contribute　to　the　improvement　of　information　sharing　efficiency　among　multiple　intelligent　agents.　Additionally,　a　spatial-temporal　feature　fusion　network　module　is　designed,　which　integrates　graph　convolutional　networks　(GCN)　and　gated　recurrent　units　(GRU).　It　can　effectively　fuse　independent　spatial-temporal　features　and　further　enhance　collaborative　control　performance.　Through　extensive　experiments　conducted　in　the　SUMO　traffic　simulator　and　comparison　with　baseline　methods,　it　is　demonstrated　that　the　ST-DRL　framework　achieves　higher　success　rates　in　mixed　traffic　scenarios　and　exhibits　better　trade-offs　between　safety　and　efficiency.　The　analysis　of　the　results　indicates　that　ST-DRL　has　increased　the　success　rate　of　the　task　by　15.6%　compared　to　the　baseline　method,　while　reducing　model　training　and　task　completion　times　by　26.6%　respectively.