To explore potential applications of GaoFen-3 (GF-3) fully polarimetric synthetic aperture radar (SAR) data in ground-feature classification, we selected Jiangxiang Town, Nanchang County as the research area and used GF-3 quad-polarization strip I (QPSI) data for classification. Pauli decomposition, Krogager decomposition, and the H  /  A  /  α decomposition model were used to decompose the GF-3 full-polarization data; combined with the backscattering coefficient values of GF-3’s HH, HV, VH, and VV polarization data, feature optimization was performed, and the optimal ground-object features for feature classification were obtained. The random forest (RF) method was used to classify the optimal features, and the results were then compared and verified. The results demonstrate that the component after polarization decomposition of H  /  A  /  α is more important in classification than the original backscattering coefficient values of HH, HV, VH, and VV. GF-3 single-phase SAR data were used for ground-feature classification in a crop-intensive area of South China, and the optimal feature optimization results included H  /  A  /  α decomposition and synthesis components of the alpha, anisotropy, red, entropy, blue, green, and polarization backscattering coefficients of VV. The RF method’s classification accuracy was higher than that of the maximum-likelihood, support-vector-machine, and neural-network methods. Using this approach, GF-3 data classification’s overall accuracy was up to 78.17%, and the Kappa coefficient was 0.738. This study has clear practical significance and reference value for the selection of classification methods for ground features based on GF- 3 data in cloudy and rainy areas.