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Seismic reservoir inversion is the main technology method for predicting underground lithology in oil and gas exploration. The traditional deep domain reservoir inversion needs to convert the depth domain data to the time domain first, and then, based on the convolution theory, complete the inversion step by step according to the sequence of well seismic calibration, establishment of the initial geological low-frequency model, and each step has its corresponding geological physical significance. Deep learning, based on multi-layer network structure, is widely used in complex nonlinear computing and has achieved good results. Direct deep domain reservoir inversion combined with seismic depth domain data can better reverse the drawbacks of traditional time domain/depth domain reservoir inversion, such as multiple steps and complex inversion parameters, improve the coincidence rate of sand bodies drilled in a single well and reduce the multi-solution of sand bodies between Wells. At the same time, it provides a new way to shorten the cycle of reservoir inversion.