Excessive　nitrogen　application　and　low　nitrogen　use　efficiency　have　been　major　issues　in　China＇s　agricultural　development,　posing　significant　challenges　for　field　management.　Nitrogen　is　a　critical　nutrient　for　crop　growth,　playing　an　indispensable　role　in　crop　development,　yield　formation,　and　quality　enhancement.　Therefore,　precisely　controlling　nitrogen　application　rates　can　reduce　environmental　pollution　caused　by　excessive　fertilization　and　improve　nitrogen　use　efficiency.　This　study　employs　multispectral　remote　sensing　images,　combined　with　field-measured　nitrogen　content,　to　develop　canopy　nitrogen　content　inversion　models　for　maize　using　three　algorithms:　backpropagation　neural　network　(BP),　support　vector　machine　(SVM),　and　partial　least　squares　regression　(PLSR).　The　results　reveal　that　there　is　a　degree　of　redundancy　in　the　information　contained　in　various　spectral　indices.　Feature　selection　effectively　eliminates　correlated　and　redundant　spectral　information,　thereby　improving　modeling　efficiency.　The　spectral　indices　Green　Index　(GI)　and　Nitrogen　Reflectance　Index　(NRI)　exhibit　strong　correlations　with　nitrogen　content　in　the　maize　canopy,　suggesting　that　the　green　and　red　spectral　bands　are　crucial　for　retrieving　maize＇s　biophysical　and　biochemical　parameters.　In　studies　on　nitrogen　content　inversion　in　the　maize　canopy,　the　random　forest　(RF)　algorithm,　coupled　with　PLSR,　demonstrated　superior　predictive　performance.　Compared　to　the　standalone　PLSR　model,　accuracy　improved　by　3.5%-6.5%,　providing　a　scientific　foundation　and　technical　support　for　precise　nitrogen　diagnosis　and　fertilizer　management　in　maize　cultivation.