In the era of big data, high-dimensional datasets have become increasingly common in fields such as biometrics, computer vision, and medical imaging. While such data contain abundant information, they are often accompanied by substantial noise, high redundancy, and complex intrinsic structures, posing significant challenges for analysis and modeling. To address these issues, unsupervised feature selection has attracted growing interest due to its ability to handle unlabeled, noisy, and unstructured data. This paper proposes a novel unsupervised feature selection algorithm based on a dual-graph autoencoder (DGA), which combines the powerful data reconstruction capability of autoencoders with the structural preservation strengths of graph regularization. Specifically, the algorithm introduces the l2,1/2-norm and l2,1-norm constraints on the encoder and decoder weight matrices, respectively, to promote feature sparsity and suppress redundancy. Furthermore, an l2,1/2-norm loss term is introduced to enhance robustness against noise and outliers. Two separate adjacency graphs are constructed to capture the local geometric relationships among samples and among features, and their corresponding graph regularization terms are embedded in the training process to retain the intrinsic structure of the data. Experiments on multiple benchmark datasets and [68Ga]Ga-Pentixafor PET/CT glioma imaging data demonstrate that the proposed DGA significantly improves clustering performance and accurately identifies features associated with lesion regions. From a clinical perspective, DGA facilitates more accurate lesion characterization and biomarker identification in glioma patients, thereby offering potential utility in aiding diagnosis, treatment planning, and personalized prognosis assessment.