Accurate water quality prediction is essential for effective ecological water management. However, water quality exhibits complex non-stationary dynamics and multi-dimensional nonlinear relationships driven by temporal evolution and environmental variability. In multi-parameter river water quality prediction, intricate spatial-temporal dependencies make it difficult for traditional models to effectively integrate dynamic topology and long-period features. To address this challenge, we propose a spatial-temporal graph convolutional network (STGCN) model. In the temporal dimension, a masked sub-series transformer module is employed to extract long-term trends through self-supervised pretraining. Combined with dilated causal convolution, it captures cumulative water quality effects and alleviates the response lag common in traditional models when facing abrupt changes. In the spatial dimension, a dynamic graph learning module integrates a predefined station-distance adjacency matrix with a dynamic residual map to generate adaptive graph structures. Experimental results demonstrate that the proposed model outperforms existing methods in water quality prediction, achieving an R2 greater than 0.93 across all water quality indicators.