Research on sea clutter modeling is meaningful for the sea clutter jamming rejection in radar detection. The previous methods cannot fit the characteristics such as the peak value and amplitude width of the sea clutter amplitude distribution curves. Thus, an estimation method of sea clutter based on the multi-feature-point model validation is proposed. First, the amplitude distribution characteristics and temporal correlation of sea clutter are analyzed, and the spherically invariant random process is used to simulate the K-distribution sea clutter model. Then, six feature points are constructed, (i.e., the maximum probability density, the amplitude value of maximum probability density, the 3 dB amplitude width, the amplitude widths corresponding to 1/3, and 2/3 of the maximum probability density, and the amplitude critical value corresponding to probability density lower than 0.01). Based on the multigroup feature points of the amplitude distribution curves, the radial basis function (RBF) neural network is trained to derive the relationship between the multiple features and the shape parameter and predict the shape parameter of the measured data. Finally, the simulation results show that the proposed method can fit the characteristics of the actual sea clutter amplitude distribution more accurately than the previous estimation methods.