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The improvement of photovoltaic power prediction accuracy is of great significance to ensure the safe and stable operation of smart grid.In order to solve the disadvantages of low prediction accuracy and slow convergence speed of traditional BP neural network, this paper proposes a short-term photovoltaic power prediction method based on particle swarm differential evolution(DE) parallel computing which optimizes BP Neural Network. First,this method analyzes the importance of the influencing factor and selects similar training sample sets through weighted Euclidean Distance.Second, the algorithm groups the Particle Swarm Optimization(PSO), and optimizesthe PSO internallyand externally via the hybrid algorithm of PSO and differential evolution,so as to ensure the PSO diversity.After that,the prediction model is established, and the PSO-DE-BP algorithm is parallelized through the memory computing platform based on spark.Finally, the model is analyzed and verified according to the prediction results of different weather types. This method has higher stability and prediction accuracy than PSO-BP and BP algorithm models.