Abstract: Physical mechanism-based models for river water quality prediction involve complicated calculations, whereas machine learning models lack interpretability, resulting in a disconnection between predictions and management decisions that hinders practical application. To deeply integrate high-precision prediction with decision support, a TCN-Attention deep learning model that combines a temporal convolutional network and an attention mechanism is constructed to predict six core water quality indicators, with Bayesian optimization used for automatic parameter tuning. The SHAP interpretability technique is introduced to quantify the contributions of multi-source input features and reveal key driving factors. Based on the Qwen3-Next large language model, water quality grade evaluations and improvement recommendations are automatically generated. Application results for rivers in Xinwu District demonstrate that the TCN-Attention model achieves good prediction performance under small-sample conditions, with domestic water use, turbidity, and water identified as the most critical influencing factors. Qwen3-Next model achieves an accuracy of 89.8% in water quality grading, and the proposed improvement recommendations are targeted and practicable. The proposed interpretable intelligent water quality prediction method effectively improves the accuracy and interpretability of urban river water quality predictions, providing a reliable technical pathway for smart water environment management.