Accurate and stable prediction of regional wind power is crucial for optimal scheduling and renewable energy utilization in the power grid. In this paper, a novel multi-objective optimized recurrent neural network with temporal pattern attention (TPA) is proposed to address the randomness and uncertainty of wind farms in regional wind power prediction. Firstly, Taguchi method is applied to select the weather variables from wind farms, reducing redundancy and improving efficiency. Then, the stacked model is constructed using a denoising autoencoder (DAE) and gated recurrent unit (GRU), to improve the robustness and temporal correlation of the hidden states. The TPA is introduced to assign different weights to the hidden states, considering the multivariate relationships at different time steps. Furthermore, the Multi-objective slime mould algorithm (MOSMA) and variable weight multi-objective loss function (VMLF) are developed to optimize DGRU-TPA under multiple objectives to realize accurate and stable prediction. Finally, the experiment results demonstrate that nRMSE, nMAPE, and nSD of the proposed model are reduced by 26.36%, 24.05%, and 21.04% respectively, and qualification rate (QR) is increased by 13.56% compared to other models. The proposed model has achieved superior performance in regional prediction, which is crucial for effective grid management with increasing wind energy.