The malicious dissemination of fake images has triggered a societal trust crisis, making deepfake detection a critical area of research. However, existing methods often rely on single-domain features, which limits their generalization to diverse forgery techniques. To address this limitation, this paper proposes a novel Multi-Feature Contrastive Learning (MFCL) model for deepfake detection. MFCL integrates multiple image features within an unsupervised contrastive learning framework. Specifically, 15 feature combinations are designed based on six image features from three domains: color (RGB, YCbCr), texture (Sobel, Prewitt), and frequency (DCT, SRM), aiming to identify the most effective multi-feature configuration for MFCL. Furthermore, a Feature Mix Module (FMix) is introduced to enhance feature fusion through depthwise separable convolutions. Extensive experiments on multiple datasets demonstrate that MFCL achieves superior detection accuracy and robustness, particularly in cross-dataset and cross-manipulation scenarios, highlighting its strong generalization capabilities.