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The calculation of gas hydrate saturation is the key parameter of reservoir optimization and resource evaluation. In view of the low accuracy of data interpretation model and the lack of model input parameters, a soft sensing model establishment method based on electrical impedance characteristic parameters and ensemble neural network was proposed. On the basis of preprocessing, feature parameter extraction and selection of impedance spectrum data, a sample set was formed. BP neural network was designed for four pairs of sensors respectively. The average method was adopted to integrate the four BP networks and then an ensemble network model was obtained. It has been demonstrated that the average relative error of hydrate saturation calculated by the ensemble network model is 3.33%, the average absolute error is 0.0014, and the root mean square error is 6.56%. The three errors of the ensemble network are lower than those of each sub network. The frequency response characteristics and characteristic parameters of the hydrate-bearing sediment can be obtained from the electrical impedance spectrum test in a wide frequency range, which can provide a large number of input parameters for the neural network model. The ensemble neural network can be used to comprehensively apply the measurement data of multiple sensors located in different measurement directions. The adverse influence of non-uniform distribution of hydrate in space on the accuracy of saturation calculation can be overcome by adopting appropriate integration strategies.