To address the challenges in network anomaly detection, such as data label imbalance and the poor performance of traditional Support Vector Machines (SVM) in fitting high-dimensional, large-sample datasets, this paper proposes a novel framework for network anomaly detection. The core contribution is the proposal of a Fuzzy Granular Support Vector Machine (FGSVM) based on fuzzy granular vectors. FGSVM constructs fuzzy granular vectors by performing fuzzy granulation on data features, thereby comprehensively capturing both local feature distributions and global data characteristics. This method granulates all data, avoiding the information loss caused by sample compression in traditional methods. To further optimize model performance, this paper also introduces a Noise-Aware Hybrid Weighted Sampling (NA-HWS) method, which optimizes the data distribution by purifying the data and applying weighted sampling to critical boundary regions. This approach combines with FGSVM to form the more powerful S-FGSVM model. Comprehensive experimental results on several internationally recognized benchmark datasets, including NSL-KDD, CIC-IDS-2017, and UNSW-NB15, confirm the significant superiority of the proposed methods. The S-FGSVM model demonstrated outstanding performance on all key metrics, significantly surpassing most baseline algorithms, including both deep learning and classic machine learning models. Compared to deep learning models, which generally have high computational overhead, the model proposed in this study shows a significant advantage in computational efficiency, achieving an ideal balance between detection performance and operational efficiency.