Fraud detection has emerged as a significant area of study, primarily due to its considerable impact on real-world applications. Despite the effectiveness of existing methods for fraud detection, they have not adequately addressed two key challenges: fraudulent camouflage and class imbalance. To tackle these challenges, we propose a novel model called Contrastive Learning on Feature and Structural Levels in Graph Neural Networks (CLFS-GNN) to effectively tackle these challenges. Our model incorporates an innovative neighbor nodes selection module that considers both feature and structural similarity between central nodes and their neighbor nodes, effectively reducing interference from fraudulent nodes by selecting highly similar neighbor nodes. Additionally, it employs an intra- and inter-graph message aggregation module with attention mechanisms to enhance the value of aggregated neighbor node information, thereby improving fraud detection performance. Furthermore, the algorithm incorporates contrastive learning to pull similar nodes closer and push dissimilar nodes further apart, mitigating class imbalance effects and achieving superior performance. Extensive experimental results show that this model outperforms the state-of-the-art GNN-based fraud detection on the Yelp and Amazon benchmark datasets.