Multimodal content combining textual and visual information poses significant challenges for rumor detection on social media. Compared to traditional spatial domain features, frequency domain features have attracted increasing attention due to their stronger discriminative capabilities. However, existing methods still fall short in capturing cross-modal semantic inconsistencies and often overlook inherent noise in multimodal features, which limits overall detection performance. To address these issues, we propose a novel multimodal rumor detection method based on multi-scale spectral selection and entropy-guided uncertainty fusion. Specifically, we first apply the Discrete Cosine Transform (DCT) to image and text features to convert them into the frequency domain. Then, multi-scale convolutional filters are employed to extract fine-grained information across different frequency scales. Next, modality separation is performed to capture both shared and modality-specific features, enabling more effective cross-modal representation learning. Finally, entropy is used to estimate the uncertainty of each prediction branch, calculate confidence scores, and perform adaptive weighted fusion accordingly. Experimental results on multiple benchmark datasets demonstrate that our method outperforms existing state-of-the-art approaches in multimodal rumor detection, demonstrating stronger detection capability and robustness.