Among　hyperspectral　imaging　technologies,interferometric　spectral　imaging　is　widely　used　in　remote　sening　due　to　advantages　of　large　luminous　flux　and　high　resolution.However,with　complicated　mechanism,interferometric　imaging　faces　the　impact　of　multi-stage　degradation.Most　exsiting　interferometric　spectrum　reconstruction　methods　are　based　on　tradition　model-based　frame-work　with　multiple　steps,showing　poor　efficiency　and　restricted　performance.Thus,we　propose　an　interferometric　spectrum　reconstruction　method　based　on　degradation　synthesis　and　deep　learning.Firstly,based　on　imaging　mechanism,we　proposed　an　mathematical　model　of　interferometric　imag-ing　to　analyse　the　degradation　components　as　noises　and　trends　during　imaging.The　model　con-sists　of　three　stages,namely　instrument　degradation,sensing　degradation,and　signal-independent　degradation　process.Then,we　designed　calibration-based　method　to　estimate　parameters　in　the　model,of　which　the　results　are　used　for　synthesizing　realistic　dataset　for　learning-based　algorithms.In　addition,we　proposed　a　dual-stage　interferogram　spectrum　reconstruction　framework,which　sup-ports　pre-training　and　integration　of　denoising　DNNs.Experiments　exhibits　the　reliability　of　our　degradation　model　and　synthesized　data,and　the　effectiveness　of　the　proposed　reconstruction　method.