Self-supervised learning (SSL) has gained significant attention for its ability to leverage large-scale unlabeled data for training, demonstrating remarkable applications in computer vision and natural language processing. However, recent studies have shown that SSL models are highly vulnerable to backdoor attacks, where adversaries can implant hidden triggers into the pretraining process, leading to compromised downstream tasks. Existing backdoor defense mechanisms, while effective to some extent, often rely on labeled data, incur high computational costs, or involve complex operations, making it difficult to achieve a balance between model performance and defense effectiveness. In this paper, we propose a backdoor defense method called UTRBD, which is based on untargeted trigger reconstruction, combined with a contrastive fine-tuning strategy. It effectively defends against backdoor attacks using only a small amount of unlabeled data and minimal computational resources, while maintaining high model performance. Through extensive experiments on the ImageNet100 and CIFAR10 datasets, our method performs excellently across various self-supervised learning models and different encoder architectures, successfully mitigating multiple types of backdoor attacks.