Car　networking　systems　based　on　5G-V2X　(vehicle-to-everything)　have　high　requirements　for　reliability　and　low-latency　communication　to　further　improve　communication　performance.　In　the　V2X　scenario,　this　article　establishes　an　extended　model　(basic　expansion　model)　suitable　for　high-speed　mobile　scenarios　based　on　the　sparsity　of　the　channel　impulse　response.　And　propose　a　channel　estimation　algorithm　based　on　deep　learning,　the　method　designed　a　multilayer　convolutional　neural　network　to　complete　frequency　domain　interpolation.　A　two-way　control　cycle　gating　unit　(bidirectional　gated　recurrent　unit)　is　designed　to　predict　the　state　in　the　time　domain.　And　introduce　speed　parameters　and　multipath　parameters　to　accurately　train　channel　data　under　different　moving　speed　environments.　System　simulation　shows　that　the　proposed　algorithm　can　accurately　train　the　number　of　channels.　Compared　with　the　traditional　car　networking　channel　estimation　algorithm,　the　proposed　algorithm　improves　the　accuracy　of　channel　estimation　and　effectively　reduces　the　bit　error　rate.