This study presents a multi-level feature fusion approach for imbalanced network traffic anomaly detection to overcome the accuracy limitations of existing methods caused by data imbalance and insufficient feature extraction. The proposed framework first employs CGAN-SMOTE algorithm to balance data distribution, then utilizes gated recurrent units with attention mechanisms to capture long-term dependencies and extract discriminative temporal local features through adaptive weight allocation. Concurrently, bidirectional long short-term memory networks with average pooling are applied to obtain comprehensive temporal global features. These extracted temporal features are subsequently fused and processed by an enhanced convolutional neural network to learn spatial representations, significantly improving anomaly recognition capability. Experimental validation on public datasets confirms the superior detection performance of our model compared to various state-of-the-art methods.