Traffic　forecasting　is　an　increasingly　important　research　topic　in　the　field　of　Intelligent　Transportation　Systems　(ITS).　In　this　field,　prediction　models　based　on　Graph　Convolution　Networks　(GCN)　have　become　very　popular.　Most　GCN-based　models　focus　on　constructing　various　optimized　or　dynamic　road　network　graphs　to　represent　the　spatio-temporal　correlation　hidden　in　traffic　data.　However,　these　methods　currently　only　consider　the　construction　of　a　single　improved　road　network　graph　and　ignore　the　relationship　of　these　existing　optimized　road　network　graphs.　Therefore,　in　this　paper,　we　propose　a　Contrastive　Optimized　Graph　Convolution　Network　(COGCN)　to　connect　two　kinds　of　optimized　road　network　graphs　and　maintain　their　global-local　feature　consistency　through　contrastive　learning.　The　proposed　COGCN　model　is　evaluated　in　detail　using　four　real　traffic　datasets:　two　traffic　speed　datasets　and　two　traffic　flow　datasets.　Experimental　results　show　that　COGCN　improves　forecasting　accuracy　by　at　least　2%　on　the　two　speed　datasets　and　9%　on　the　two　flow　datasets　compared　to　the　existing　state-of-the-art　GCN-based　methods.