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The prediction of global horizontal irradiance has become an effective technique to address the intermittence issue of photovoltaic (PV) power generation. This article proposes a novel deep neural network(DNN), named Mixer Dual Path Network (Mixer-DPN), for promising solar forecasting. It shares common features of cloud images and maintains the flexibility to explore new features through dual-path architecture by combining the Mixer layer and Dual Path Network. Therefore, the proposed model can provide more accurate prediction results compared to the classical DNN-based predictors. Moreover, the proposed model shows a faster convergence speed and smaller model size, which makes it suitable for a practical global horizontal irradiance. The merits of the proposed model are verified by testing it with the data from National Renewable Energy Laboratory comparing it with other DNN-based prediction models. Studies have shown that the new model has achieved excellent results in MSE, MAE and other indicators, and the R2 prediction accuracy rate has increased by 14% compared with the baseline model.