The photovoltaic power (PV) power between similar days has great similarity. To further improve the accuracy of PV power prediction, this paper improves the conventional similar day selection method and proposes a combined PV power prediction method based on the theory of multi-temporal similarity day. The method firstly establishes the time-sensitive meteorological feature quantity, and then selects similar time periods in different time periods by using the similarity combination index to construct multiple combined similar days, and the combined similar days further guarantee the similarity of each time period, and constructs the PSO-LightGBM-BiLSTM time-varying weight combined prediction model to improve the prediction model accuracy and make up for the shortcomings of a single model. Finally, the PSO-BiLSTM model is used to correct the PV power of the day to be predicted. In this paper, the feasibility and superiority of the proposed method are verified by using the data of a PV plant in Guangdong, China as an example.