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Crop fine classification is of great significance in many fields such as agricultural resources survey and crop planting structure supervision. Polarimetric synthetic aperture radar (PolSAR) can effectively detect camouflage and penetrate masks, extract multiple scattering feature information, obtain continuous time series information covering the key climatic phases of crop growth, and effectively enhance the richness of crop remote sensing features, which is a unique advantage in crop classification. However, the introduction of multi-temporal phases and multi-features inevitably leads to a drastic increase in model arithmetic, which is not conducive to engineering applications. In view of the above problems, a multi-feature optimization-based approach for crop fine classification of PolSAR data was proposed, which firstly carried out multiple polarization target decomposition and parameter extraction of the PolSAR data in order to obtain multiple scattering features, and then a stacked sparse self-coding network based and ReliefF preferred method was used for feature enhancement and optimization to obtain the optimal set of features, and finally a convolutional neural network with two branching structures was constructed to fuse the features output from different convolutional depths to complete the high-precision classification of crops. Through the characterization of single-time-phase data, the preliminary classification experiments of single-time-phase data and the comparison experiments of combining classifiers with different feature sets of multi-time-phase data, it was proved that the method proposed can maximally extract the differential features between different crops under the premise of low-dimensional feature input, and accurately and efficiently realize the fine classification of crops, with the highest classification accuracy and Kappa coefficient reaching 97.69% and 97.24%, respectively.