Aiming　at　the　problems　of　dynamic　target　interference　and　occlusion　in　complex　scenes　of　monocular　visual　localization　system,　a　feature-enhanced　visual　localization　algorithm　based　on　attention　mechanism　is　proposed.　The　image　depth　information　and　pose　information　are　fused　by　the　depth　estimation　network　and　the　pose　estimation　network,　and　the　tightly　coupled　training　of　the　two-branch　network　is　realized　by　using　the　view　reconstruction　loss.　In　order　to　further　improve　the　feature　representation　ability　of　the　network,　the　occlusion　mask　module　and　the　attention　mechanism　module　are　respectively　integrated,　the　binary　mask　is　constructed　using　the　depth　information　of　adjacent　images　and　the　photometric　loss　is　optimized,　and　the　saliency　of　key　features　is　improved　by　using　channel　attention　and　spatial　attention.　The　experimental　results　show　that　the　algorithm　can　effectively　improve　the　feature　representation　ability　of　the　network,　alleviate　the　problem　of　scale　error　accumulation　under　long-term　sequence　tracking,　and　obtain　higher　localization　accuracy.　©　2023　IEEE.