In order to improve the forecasting software aging trend, a New Particle Swarm Optimization Simulated Annealing algorithm (NPSOSA) is proposed to optimize the weights and thresholds of BP neural network, and then NPSOSA-BP neural network forecasting model is constructed. The software aging test platform was built to collect the required aging data and conduct simulation training. The experimental results show that the NPSOSA-BP neural network model improves the prediction accuracy and applicability compared with the BP neural network model optimized by the traditional Particle Swarm Optimization (PSO) and the traditional Particle Swarm Optimization Simulated Annealing algorithm (PSOSA). The validity of this method is verified in this application field.