Due to its open-source nature, the Android operating system has consistently been a primary target for attackers. Learning-based methods have made significant progress in the field of Android malware detection. However, traditional detection methods based on static features struggle to identify obfuscated malicious code, while methods relying on dynamic analysis suffer from low efficiency. To address this, we propose a dynamic weighted feature selection method that analyzes the importance and stability of features, calculates scores to filter out the most robust features, and combines these selected features with the program's structural information. We then utilize graph neural networks for classification, thereby improving the robustness and accuracy of the detection system. We analyzed 8,664 malware samples from eight malware families and tested a total of 44,940 malware variants generated using seven obfuscation strategies. Experiments demonstrate that our proposed method achieves an F1-score of 95.56% on the unobfuscated dataset and 92.28% on the obfuscated dataset, indicating that the model can effectively detect obfuscated malware.