Robust monocular SLAM is challenged by image distortions and non-uniform keypoint distributions. This paper introduces SED-SLAM, a novel system enhancing distortion resilience through spatially equalized deep feature extraction. At its core, a lightweight Mobile-Superpoint network extracts discriminative features, while our proposed Spatially Adaptive Thresholding (SAT) module ensures their uniform spatial coverage by adaptively regulating keypoint selection. Experiments on public benchmarks demonstrate that SED-SLAM achieves superior trajectory accuracy and robustness under various image distortions with real-time efficiency. These results validate that jointly optimizing feature quality and spatial balance is critical for enhancing SLAM performance in challenging environments.