提出基于主成分分析和归一化RBF神经网络优化相结合的财务综合评价方法,克服传统神经网络财务分析中的评价指标偏少、存在局部极小等不足,为公司财务评价提供新的思路和方法,并利用神经网络模型对我国钢铁业上市公司2007年财务状况进行仿真实验,为分析者决策提供准确、可靠的参考依据。 Proposed a financial comprehensive evaluation method based on the combination of principal component analysis and normalized RBF neural network optimization to overcome the shortcomings of the traditional neural network in the financial analysis of the evaluation index is too small, there is a local minimum and so on, providing new ideas for the company’s financial evaluation and Method, and uses the neural network model to simulate the financial status of listed companies in China’s steel industry in 2007, providing an accurate and reliable reference for the decision-making of analysts.