Code readability is a critical aspect of program comprehension and an important metric for overall software quality.Recently, many deep learning-based code readability classification models have been proposed.Although they reached state-ofthe-art classification results, we consider that their performance is limited due to the shortage of labeled data.To address this problem, we propose WCL-CRC, a two-stage weakly-supervised contrastive learning framework for few-shot code readability classification, which pre-trains existing models using contrastive learning techniques with a large amount of weakly-labeled data obtained from open-source repositories and then fine-tunes them with a few labeled data.We conduct experiments on Java and Python datasets.The results show that applying WCL-CRC to existing code readability classification models can improve their Accuracy by 0.21% to 2.43%, F-Measure by 0.52% to 2.66%, AUC by 0.14% to 1.58%, and MCC by 1.06% to 8.73%, indicating that our approach effectively learns readability-related information from a large amount of weakly-labeled data and significantly improves code readability classification performance in situations where labeled data is limited.