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Accurate short-term forecast of photovoltaic power generation plays an important role in smart energy management system of microgrid. This paper proposes a cnN-BigRU model for short-term pv power generation prediction based on attention mechanism. The core idea is to extract the spatial features of photovoltaic data through CNN and send these spatial features extracted by CNN to BiGRU neural network. BiGRU model is used to capture the bidirectional information flow of photovoltaic temporal data set and learn the dynamic change rules of photovoltaic features. The Attention mechanism is introduced to assign weight to the output of the hidden layer of CNN-BigRU, reduce the information loss caused by the excessively long time sequence, highlight the influence of strong correlation features, and reduce the influence of weak correlation features. The experimental results show that the proposed model has better prediction accuracy than BP neural network, GRU, BiGRU, BiLSTM based on Attention mechanism and BiGRU based on Attention mechanism.