This　study　used　a　multigrid-based　convolutional　neural　network　architecture　known　as　MgNet　in　operator　learning　to　solve　numerical　partial　differential　equations　(PDEs).　Given　the　property　of　smoothing　iterations　in　multigrid　methods　where　low-frequency　errors　decay　slowly,　we　introduced　a　low-frequency　correction　structure　for　residuals　to　enhance　the　standard　V-cycle　MgNet.　The　enhanced　MgNet　model　can　capture　the　low-frequency　features　of　solutions　considerably　better　than　the　standard　V-cycle　MgNet.　The　numerical　results　obtained　using　some　standard　operator　learning　tasks　are　better　than　those　obtained　using　many　state-of-the-art　methods,　demonstrating　the　efficiency　of　our　model.　Moreover,　numerically,　our　new　model　is　more　robust　in　case　of　low-　and　high-resolution　data　during　training　and　testing,　respectively.