[Objective] Accurate multi-regional load forecasting (MRLF) is a critical foundation for ensuring grid stability and optimizing the economic dispatch of modern power systems. Traditional load forecasting methods face challenges in capturing the dynamic spatial dependencies among multi-regional loads. To address the problem that traditional methods are unable to extract dynamic spatial features among loads, a MRLF model based on decoupling and adversarial graph attention network (DAGAT) is proposed.[Methods] The model effectively extracted the dynamic spatial features between multi-regional loads through graph attention networks. Firstly, the regional load series was decomposed into trend and fluctuation components using discrete wavelet transform(DWT). Secondly, a two-channel feature extraction module was designed to extract the spatio-temporal features of the multi-regional load series based on the different characteristics of trend and fluctuation components. In addition, a generative adversarial network architecture based on zero-sum game was introduced for adversarial training of spatio-temporal prediction models, the resulting adversarial loss was weighted and combined with a traditional forecasting loss function as the loss function of the model. Finally, simulation validation was performed based on real multi-regional load data from the New York independent system operator. [Results] Comparison and analysis with mainstream models showed that the DAGAT model proposed in this paper was more superior than traditional machine learning models, prediction models that only capture temporal dependencies, and prediction models that express spatial correlations using static weight matrices. The superiority of the model stemmed from the fact that the graph attention network captured the spatial correlation between multi-regional loads by learning dynamically changing weights, and the DWT clearly represented the characteristics of the multi-regional load series, and the joint loss further optimized the model parameters. [Conclusion] DAGAT effectively improves the accuracy of MRLF and solves the problem that dynamic spatial features between multi- regional loads cannot be captured, which is effective and superior in engineering practice.