Predicting the trend of inlet valve temperature changes provides significant references for the operating status of valve cooling systems. Since the traditional methods have problems such as a large time span of data collection and sensor deviation, this study proposes a Robust-InTemp prediction model for inlet valve temperature based on adversarial perturbation and local information enhancement. Specifically, Robust-InTemp enhances the model’s generalization ability and noise resistance robustness by adding rule-based Gaussian noise to the original data and employing projected gradient descent (PGD) for adversarial training. Meanwhile, relative positional encoding, one-dimensional convolution, and gated linear units (GLUs) are introduced to enhance the model’s ability to learn local features, thus improving prediction accuracy. Experimental results show that compared to various benchmark models, Robust-InTemp has clear advantages in predictive performance and anti-interference ability. Additionally, further ablation experiments validate the effectiveness of each component in the model.