Graph Neural Networks (GNNs) have demonstrated remarkable success in various fields, however, they face challenges in deep network architectures, such as over-smoothing, sensitivity to topological perturbations, and limitations on heterogeneous graphs. This paper proposes a Self-Attention Propagation Network based on Contrastive Augmentation (SAMPCA) to address these issues. SAMPCA uses a novel multi-dimensional graph perturbation graph data augmentation method and introduces graph regularization to optimize the graph structure. It also incorporates a self-attention multiscale mixed mechanism for adaptive propagation, mitigating over-smoothing and enriching neighborhood information diversity. Furthermore, SAMPCA extends edge weights to negative values to better adapt to complex heterogeneous graph topologies. Experiments demonstrate that SAMPCA effectively alleviates over-smoothing and outperforms SOTA models in semi-supervised node classification tasks across multiple datasets. On homogeneous graphs like Cora, SAMPCA achieved an improvement of 2.23% over GPRGNN. On heterogeneous graphs, it demonstrated remarkable improvement on the Texas dataset, outperforming GPRGNN by 1.7%. These results showcase its augmented generalization and robustness.