With the rapid development of time-domain surveys, the availability of large-scale light-curve data offers new opportunities for studying stellar evolution and variable-star classification, while simultaneously posing challenges for feature extraction and modeling. We present StarCLR, a contrastive pretraining framework for large-scale light curves. By constructing positive pairs from partially overlapping subsequences, StarCLR learns robust temporal representations. We pretrain StarCLR on the Transiting Exoplanet Survey Satellite (TESS) dataset and fine-tune it for variable-star classification across three surveys with distinct observational characteristics: TESS (18 types), the Zwicky Transient Facility (ZTF; 11 types), and Gaia (24 types). StarCLR achieves macro-F1 scores of 84.35%, 87.82%, and 92.73%, and micro-F1 scores of 94.46%, 92.83%, and 99.49%, respectively. Compared with LSTM and transformer models trained from scratch, StarCLR performs better on TESS and ZTF, with the largest gains on sparsely sampled ZTF light curves, demonstrating promising generalization. For Gaia, which involves a broader class space, the evaluation is not directly comparable, and performance is likely influenced by astrophysical feature differences, resulting in a more limited contribution from the pretrained backbone. Systematic ablations on embedding design, pooling strategy, and pretraining settings further indicate that the pretrained representations provide performance gains by capturing informative temporal characteristics of light curves. Looking ahead, with standardized datasets and more diverse labeling schemes, the generalization ability of StarCLR can further be enhanced. The inference code and pretrained model weights are made available on GitHub (https://github.com/dj-y/StarCLR-Inference).