Performance evaluation research on social capital improving the livelihood economy in West China

Abstract

In order to enhance the effectiveness of performance evaluation research on social capital improving the livelihood economy in west China, this paper puts forward a performance evaluation method-particle swarm optimization neural network algorithm based on the self-adaptive genetic operator of social capital improving the livelihood economy in west China. Firstly, it establishes the comprehensive performance evaluation index system of the livelihood economy in west China, combined with the depth neural network, establishes the network structure model of the livelihood economy in west China; secondly, it designs the particle swarm optimization algorithm based on self-adaptive genetic operator to optimize the weight of neural network algorithm and realize the effective analysis on the livelihood economy in west China; finally, it verifies the effectiveness of algorithm through empirical analysis.