Semi-supervised anomaly detection methods have demonstrated significant performance improvements over unsupervised anomaly detection methods. In real-world scenarios, the definition of anomalies often relies more on prior domain knowledge than on differences in distributions. In this paper, we propose CLEAR: Contrastive Learning EnhAnced semi-supeRvised anomaly detection. Firstly, CLEAR adopts a semi-supervised contrastive learning strategy to learn an enhanced representation with prior domain knowledge. CLEAR then processes potential nonlinear patterns within the representations using a kernelized mapper, and further analyzes the distribution of samples across each dimension to compute the final anomaly scores. Extensive experiments conducted on a real-world shopping orders dataset from JD.com as well as 12 real-world benchmark datasets, demonstrate that CLEAR significantly outperforms current semi-supervised anomaly detection methods.