This paper proposed an innovative WTC-LSTM-ASSA-NSTformer framework for gold price forecasting. The model integrates Wavelet Transform Convolution, Long Short-Term Memory networks (LSTM), and an improved Nyström Spatial-Temporal Transformer (NSTformer) based on Adaptive Sparse Self-Attention (ASSA), effectively capturing the multi-scale features and long- and short-term dependencies of gold prices. Additionally, for the first time, various financial and economic uncertainty indices (including VIX, GPR, EPU, and T10Y3M) are innovatively incorporated into the forecasting model, enhancing its adaptability to complex market environments. An empirical analysis based on a large-scale daily dataset from 1990 to 2024 shows that the model significantly outperforms traditional methods and standalone deep learning models in terms of MSE and MAE metrics. The model’s superiority and stability are further validated through multiple robustness tests, including varying sliding window sizes, adjusting dataset proportions, and experiments with different forecasting horizons. This study not only provides a highly accurate tool for gold price forecasting but also offers a novel methodological pattern to financial time series analysis, with important practical implications for investment decision-making, risk management, and policy formulation.