There is a power prediction problem of wind solar complementary grid connected power generation in that the feedforward (BP) neural network can easily fall into a local optimization, resulting in the reduction of prediction accuracy. Thus an Adaptive Salp Swarm Algorithm (ASSA) optimized BP neural network wind solar complementary grid connected power prediction model is proposed. First, dynamic weight strategy and a mutation operator are introduced into the standard Salp Swarm Algorithm (SSA) to construct the ASSA. Secondly, a BP neural network algorithm is introduced to construct the wind solar complementary grid connected power prediction model. Finally, the weight and threshold of the BP network are optimized by the ASSA algorithm, and the power prediction model is thus proposed. The simulation results show that the relative error of power generation data predicted by the ASSA-BP model is less than that predicted by the BP model. The average absolute error of ASSA-BP and SSA-BP models is smaller, and the average absolute error of ASSA-BP model is the smallest, and the prediction stability of the ASSA-BP model is the strongest. The prediction model has higher accuracy than the traditional wind solar complementary grid connected power prediction method. This work is supported by Henan Joint Key Project of National Natural Science Foundation of China (No. U1804262).