In　order　to　conserve　power　resources,　researchers　have　been　increasing　their　focus　on　load　monitoring　and　identification　technologies.　However,　current　image　classification　methods　for　load　identification　face　difficulties　in　extracting　overall　image　contour　features　and　complementing　them　based　on　image　detail　textures.　To　address　this　problem,　we　propose　the　MSFEN-AM　network　model,　which　consists　of　five　parts:　input　part,　downsampling　module,　bottlenick,　upsampling　module,　and　output　part.　We　design　a　multi-scale　feature　dense　extraction　module　in　the　network　for　feature　reuse　to　improve　information　utilization.　Additionally,　we　incorporated　an　attention　mechanism　to　focus　on　extracting　texture　and　edge　information　from　the　image.　Furthermore,　we　introduced　an　attention　gate　mechanism　to　strengthen　network　selective　features　and　suppress　invalid　information.　Finally,　we　conducted　experiments　on　the　PLAID　dataset　and　performed　ablation　tests　on　the　multi-scale　feature　extraction　modules　and　attention　gating　mechanism.　The　results　show　the　superiority　of　our　method.　©　2023　IEEE.