The linear combination of various models can effectively avoid the disadvantages of poor convergence and low reliability for photovoltaic power forecasting. Simplifying the linear relationship between a single model in a linear combinatorial model can simplify the calculation of the combinatorial model, but reduce the prediction accuracy. Aiming at this problem, a prediction model based on Sparrow Search Algorithm (SSA) was proposed to optimize Bidirectional Long Short-Term Memory (BiLSTM) nonlinear combination method. Firstly, the Kernel-based Fuzzy C-means (KFCM) and Variational Modal Decomposition (VMD) are used to preprocess the original data samples. Then, using the Elman and SSA-BiLSTM forecast after photovoltaic (PV) power of pretreatment; Finally, the nonlinear combination of the two single models is optimized by the sparrow search algorithm to establish a nonlinear combination prediction model for short-term photovoltaic power. A comparative calculation example is established based on the measured data of a photovoltaic power plant, and the results showed that the average RMSE and MAE values of the proposed combined model under different weather conditions are 0.689 kW and 0.540 kW, respectively, which are superior to other comparative models, verifying the effectiveness and superiority of the proposed combined model.