Channelrhodopsins (ChRs) are essential tools in optogenetics, playing a key role in analyzing and modulating neural circuits. However, traditional experimental methods for screening high performance ChR variants are costly, inefficient, and yield scarce data, making accurate function prediction and efficient screening challenging. To address this, we propose PRNet, a deep learning architecture based on contrastive ranking networks. It jointly encodes ChR variant sequences and structures, and uses a pairwise ranking strategy to transform regression into ranking, thereby highlighting critical features through pairwise comparisons. This expands the original 163 samples into 13,203 training pairs, effectively tackling the small-sample problem. PRNet employs depthwise separable 3D convolutions for efficient feature extraction, Bi-LSTM to model local sequence dependencies, and an SGA attention mechanism to capture residue relationships, significantly boosting prediction accuracy. Experimental results show that PRNet outperforms existing models, achieving prediction accuracies of 89%, 90%, and 91% for photocurrent strength, wavelength sensitivity of photocurrents, and off-kinetics, respectively. This study offers a novel solution to the precision issue in ChR protein prediction with small-samples, setting a new paradigm for protein engineering.