Radio　frequency　interference(RFI)will　pollute　the　weak　astronomical　signals　received　by　radio　telescopes,which　in　return　will　seriously　affect　the　time-domain　astronomical　observation　and　research.In　this　paper,we　use　a　deep　learning　method　to　identify　RFI　in　frequency　spectrum　data,and　propose　a　neural　network　based　on　Unet　that　combines　the　principles　of　depthwise　separable　convolution　and　residual,named　DSC　Based　Dual-Resunet.Compared　with　the　existing　Unet　network,DSC　Based　Dual-Resunet　performs　better　in　terms　of　accuracy,F1　score,and　MIoU,and　is　also　better　in　terms　of　computation　cost　where　the　model　size　and　parameter　amount　are　12.5％of　Unet　and　the　amount　of　computation　is　38％of　Unet.The　experimental　results　show　that　the　proposed　network　is　a　high-performance　and　lightweight　network,and　it　is　hopeful　to　be　applied　to　RFI　identification　of　radio　telescopes　on　a　large　scale.