Outdoor scenes are rich in feature points with diverse geometric shapes and scales; however, significant illumination variations and high texture repetitiveness often lead to low feature extraction and matching accuracy in conventional visual simultaneous localization and mapping (SLAM) algorithms during 3D reconstruction. To improve mapping accuracy and robustness in complex environments, this paper proposes a visual SLAM algorithm integrated with an attention mechanism, aiming to enhance the feature extraction and matching strategies within SLAM systems. Specifically, a channel-spatial convolutional attention module is embedded into the convolutional layers of the SuperPoint encoder to strengthen the model’s feature detection and matching capabilities. The improved SuperPoint network is then integrated with the backend of the ORB-SLAM2 algorithm, enabling more accurate pose estimation and map construction in complex scenarios. The proposed approach is validated on the KITTI dataset. Experimental results demonstrate that the SuperPoint network integrated with the channel-spatial convolutional attention module significantly improves feature matching accuracy between images while maintaining the stability of keypoints and the discriminability of descriptors. Compared with the original ORB-SLAM2 algorithm, the proposed method achieves a 30.05% reduction in absolute trajectory error (ATE) and a 14.49% reduction in relative pose error (RPE). These results confirm that the proposed SLAM algorithm exhibits stronger robustness and stability in outdoor environments characterized by significant illumination changes and repetitive textures, effectively enhancing the mapping accuracy of SLAM systems in complex outdoor scenes.