For the severe challenges brought by the fluctuation and randomness of photovoltaic power generation to the load prediction of the dispatching department and the safe operation of the power grid, this study proposes a photovoltaic power prediction method of bidirectional long short-term memory (BiLSTM) optimized by variational modal decomposition (VMD) and cuckoo search (CS) algorithm. Firstly, VMD is employed to decompose the photovoltaic power sequence into sub-modes with different frequencies, and Pearson correlation analysis is adopted to determine the key meteorological factors affecting each mode. Secondly, the hybrid photovoltaic power prediction models of attention mechanism (AM) and BiLSTM are constructed, and the CS algorithm is utilized to obtain the optimal weight and threshold of the network. Finally, the prediction results of different modes are superimposed to obtain the final prediction results. The effectiveness of the proposed model is verified by predicting the output power of photovoltaic power stations in Arizona.