Effectiveness Analysis of Entrepreneurial Method with Computer Data Simulation

Abstract

Entrepreneurship has permeated all parts of society as a result of society’s development. College students are the main force of today’s entrepreneurship, and this work takes college students as the object of research. College students should focus not only on quantity but also on quality. In recent years, college students have engaged in a greater number of entrepreneurial activities, although their efficacy and influence have been limited. To improve the effectiveness of college students’ entrepreneurial methods, it is necessary to analyze their entrepreneurial methods to evaluate their effectiveness for method. This work presents a neural network for evaluating the efficacy of entrepreneurial strategies and employs computer data simulation technologies to validate effectiveness using a neural network and computer data simulation. First, using characteristics of global optimization of particle swarm optimization (PSO), a dynamic domain population model is proposed to improve it. The new model is with a K-means clustering algorithm and aims to increase the diversity of the population. The enhanced PSO (KPSO) algorithm has a better global optimization ability, thus particles are less likely to fall into local extreme values. Second, maintain consistency between the particle dimension size in KPSO and the weights and thresholds in the BP network and create a mapping link between KPSO and the weights and thresholds. After determining the topology for the neural network, the improved BP network (KPSO-BP) is utilized to analyze the effectiveness of college students’ entrepreneurial methods. Third, the computer data simulation results verify the effectiveness and correctness of the proposed method.