Missing　trace　reconstruction　is　an　essential　step　in　the　seismic　data　processing.　Various　interpolation　methods　have　been　proposed　for　handling　this　issue.　In　recent　years,　deep　learning-based　interpolation　techniques,　especially　convolutional　neural　networks　(CNNs),　have　been　widely　studied.　Typically,　these　studies　target　regularly/randomly　missing　cases,　leaving　consecutively　missing　situations　not　handled　properly.　In　this　article,　we　propose　a　hybrid　loss　function　$\text　{SSIM}+L_{1}$　,　based　on　structural　similarity　(SSIM)　and　$L_{1}$　norm,　for　network　training　and　attention　mechanism　as　a　network　component　that　explicitly　utilizes　global　information.　We　further　design　a　CNN　equipped　with　the　hybrid　loss　and　attention　mechanism　for　successively　missing　trace　reconstruction.　Experiments　on　synthetic　and　field　data　demonstrate　that　our　network　can　reconstruct　more　reasonable　results　than　networks　without　attention　mechanism　in　large　gap　situation　and　$\text　{SSIM}+L_{1}$　loss　promotes　interpolation　results.　We　also　discuss　the　setup　of　key　hyperparameters　of　the　network　by　a　thorough　ablation　study.