Research on Optimization of Enterprise Financial Performance Evaluation System Based on RBF Neural Network Algorithm in the Era of Data Analysis

The increasing complexity of multi-source heterogeneous financial data requires intelligent nonlinear modeling approaches capable of efficient feature extraction and adaptive decision-making. To address the limitations of conventional financial performance evaluation systems, this study proposes an optimized enterprise financial performance evaluation framework based on a Radial Basis Function (RBF) neural network. A multidimensional indicator system covering profitability, solvency, operational capability, growth potential, cash flow, and financial risk control is first reconstructed, and factor analysis is employed to eliminate redundant variables and generate compact feature representations for network training. A three-layer RBF neural network is subsequently established to perform nonlinear mapping between financial indicators and comprehensive performance scores, enabling objective evaluation without subjective weight assignment. Empirical validation using enterprises from multiple industries demonstrates that the proposed model achieves superior evaluation accuracy, faster convergence, enhanced generalization capability, and improved dynamic prediction performance compared with traditional assessment approaches. The integration of factor analysis and RBF learning effectively captures latent nonlinear relationships embedded in complex financial data and provides a scalable intelligent evaluation architecture. Beyond financial management, the proposed feature extraction and nonlinear inference strategy offers valuable methodological references for multidimensional signal analysis, adaptive pattern recognition, electromagnetic information processing, and antenna-assisted intelligent sensing systems that require robust modeling of heterogeneous data streams.