<p>Anomaly detection in multivariate time series is critical for applications such as industrial maintenance, early warning systems, and environmental monitoring. However, existing reconstruction-based models often exhibit poor generalization and high false-negative rates, limiting their effectiveness. To address these challenges, this paper proposes a time–frequency contrastive learning (TFCL) anomaly detection model that integrates a contrastive learning module with a gated feature fusion module. The contrastive learning module employs a time-weighted and temperature-controlled loss function to enhance feature representation from both time and frequency perspectives. Meanwhile, the feature fusion module utilizes a gating mechanism to dynamically adjust the integration of time and frequency features, generating robust joint representations. Additionally, a latent-feature-driven and component-weighted anomaly discrimination strategy is proposed, leveraging reconstruction residuals and feature importance to precisely identify anomalies. The model’s performance was evaluated on publicly available datasets (SWaT and WADI) and real-world air quality monitoring data (AQMD). Experimental results demonstrate that TFCL achieves an average F1 score of 92.47% on SWaT and WADI, outperforming state-of-the-art methods and demonstrating good generalization. Moreover, the proposed TFCL achieves accurate anomaly detection in AQMD, which highlights its practical value in real-world scenarios.</p>