In order to improve the prediction accuracy of software aging, a New Adaptive Genetic Simulated Annealing algorithm (NAGSA) is proposed to optimize the BP neural network prediction model. The model's selection operator is combined with the elite retention strategy using the roulette selection method, stretching the fitness function by simulated annealing algorithm in the late iteration. Compared with the traditional Adaptive Genetic Algorithm (AGA), it can adaptively adjust the crossover probability and mutation probability nonlinearly when the individual fitness is low, thereby optimizing and weighting the BP neural network weights and thresholds, injecting a memory leak code into the online book-sending website to age it, collecting the aging data required for the experiment for simulation training. The experimental results show that the BP neural network model optimized by the NAGSA-BP model compared with the traditional Genetic Algorithm (GA), traditional AGA, and traditional Adaptive Genetic Simulated Annealing algorithm (NGSA) improves the prediction accuracy and achieves excellent results. The effectiveness of the proposed method is verified in this application field.