Wind power forecasting plays a crucial role in enhancing the stability and reliability of power system operations. However, accurately wind power prediction is challenging due to its randomness and discontinuity. Most current methods tend to offer a singular predicted value, which inevitably imbued with errors, the errors precipitate a cascade of stability issues within the power grid. In order to address this issue, this study proposed a novel model performed both point and interval forecasting of wind power, leveraging the synergistic capabilities of graph convolutional neural networks (GCN), long short-term memory neural networks (LSTM) and quantile regression. Firstly, the adjacency matrix was constructed by leveraging the pearson correlation between historical wind power data and meteorological variables. Secondly, the matrix and original data served as the input of GCN. Thirdly, the output of the GCN was fused with the original data, and then, the hybrid data was taken as the input of LSTM. Finally, pinball loss was used as the training objective to obtain multiple quantile values of wind power. The experimental findings indicated that the proposed model not only achieved the lowest error rates in point forecasting but also provided the most precise and dependable prediction intervals across various confidence levels.