To address the issues of complex mapping processes, difficult parameter tuning, and poor generalization in traditional simultaneous localization and mapping (SLAM) algorithms, this paper proposes a wheeled robot experience map construction method based on spatial multi-scale continuity feature extraction. First, PSA and ASPP modules are integrated into the ResNet18 architecture. PSA groups intermediate features and calculates attention weights across channels to capture multi-scale information, thereby enhancing feature representation. ASPP incorporates dilated convolutions with varying dilation rates and global average pooling to aggregate global contextual information, further strengthening the representation of spatial multi-scale continuity features. Then, the improved ResNet-PSA-ASPP model is trained on datasets collected in both the donkey_sim simulator and real-world robot racetrack scenarios. Finally, model performance is evaluated in both simulated and real-world environments using the donkey_sim simulator and the robot operating system （ROS）. Experimental results show that the proposed model reduces steering angle prediction errors by 38.47%, 44.34%, and 35.51%, respectively, and significantly outperforms classical networks such as ResNet18, ResNet50, and VGGNet in feature extraction capability, computational efficiency, and mapping accuracy.