Many　solutions　for　imaging　through　a　scattering　medium　are　sensitive　to　noise,　which　can　lead　to　degradation　or　even　to　a　failure　of　the　image　quality.　This　is　especially　the　case　in　practical　application　scenarios,　which　are　always　filled　with　changing　ambient　light　interference;　as　such,　the　traditional　methods　are　difficult　to　practically　apply.　Therefore,　in　this　paper,　a　spatial-frequency　dual-domain　learning　neural　network　is　designed　for　reconstructing　the　target　of　a　speckle　pattern　under　different　intensities　of　ambient　light　interference.　The　network　is　mainly　based　on　two　modules.　One　module　is　designed　from　two　perspectives,　frequency　domain　denoising　and　the　spatial-frequency　spectrum　of　the　speckle　pattern.　Another　module　is　a　dual-feature　fusion　attention　module,　which　is　used　to　improve　the　accuracy　of　the　network.　The　experimental　results　demonstrate　that　the　network　is　capable　of　reconstructing　complex　targets　with　high　quality　under　varying　intensities　of　interfering　light.　Furthermore,　it　is　not　constrained　by　the　optical　memory　effect,　exhibiting　remarkable　robustness　and　generalizability.　The　research　based　on　this　paper　provides　a　feasible　path　for　the　practical　application　of　scattering　imaging　methods.