The　influence　of　some　seemingly　anomalous　samples　on　modeling　is　often　ignored　in　the　quantitative　prediction　of　soil　composition　modeling　with　hyperspectral　data.　Soil　spectral　transformation　based　on　wavelet　packet　technology　only　performs　pruning　and　threshold　filtering　based　on　experience.　The　feature　bands　selected　by　the　Pearson　correlation　coefficient　method　often　have　high　redundancy.　To　solve　these　problems,　this　paper　carried　out　a　study　of　the　prediction　of　soil　total　iron　composition　based　on　a　new　method.　First,　regarding　the　problem　of　abnormal　samples,　the　Monte　Carlo　method　based　on　particle　swarm　optimization　(PSO)　is　used　to　screen　abnormal　samples.　Second,　feature　representation　based　on　Shannon　entropy　is　adopted　for　wavelet　packet　processing.　The　amount　of　information　held　by　the　wavelet　packet　node　is　used　to　decide　whether　to　cut　the　node.　Third,　the　feature　bands　selected　based　on　the　correlation　coefficient　and　the　competitive　adaptive　reweighted　sampling　(CARS)　algorithm　using　the　least　squares　support　vector　regression　(LSSVR)　are　applied　to　the　soil　spectra　before　and　after　wavelet　packet　processing.　Finally,　the　Fe　content　was　calculated　based　on　a　1D　convolutional　neural　network　(1D-CNN).　The　results　show　that:　(1)　The　Monte　Carlo　method　based　on　particle　swarm　optimization　and　modeling　multiple　times　was　able　to　handle　the　abnormal　samples.　(2)　Based　on　the　Shannon　entropy　wavelet　packet　transformation,　simple　operations　could　simultaneously　preserve　the　spectral　information　while　removing　high-frequency　noise　from　the　spectrum,　effectively　improving　the　correlation　between　soil　spectra　and　content.　(3)　The　1D-CNN　with　added　residual　blocks　could　also　achieve　better　results　in　soil　hyperspectral　modeling　with　few　samples.