Optimization of BP neural network and particle swarm optimization for intelligent elderly care talent cultivation driven by innovation and market demand

Abstract

This paper proposes an optimized Backpropagation (BP) neural network for improving intelligent elderly care talent training. To address BP’s limitations, including noise sensitivity and slow convergence, we introduce Particle Swarm Optimization (PSO) to refine network weights and thresholds. The model integrates course quality, teacher effectiveness, platform support, and market demand, aiming to optimize elderly care service talent cultivation. Experimental results demonstrate a significant improvement in prediction accuracy, with average error reduced from 9.94% to 6.3%. This enhanced model offers a more efficient and accurate solution for aligning educational outcomes with industry needs.