Construction and Application of Talent Evaluation Model Based on Nonlinear Hierarchical Optimization Neural Network

Area of Science:

• Business Management
• Artificial Intelligence
• Neural Networks

Background:

• Traditional talent evaluation models face challenges with high costs and computational demands.
• Effective talent assessment is crucial for successful business management.
• Developing efficient and accurate talent evaluation methods is an ongoing research problem.

Purpose of the Study:

• To develop and validate a new talent evaluation model using a nonlinear hierarchical optimization neural network.
• To address the limitations of traditional talent assessment methods.
• To improve the accuracy and efficiency of enterprise talent evaluation.

Main Methods:

• Development of a talent evaluation index system based on nonlinear hierarchical optimization neural network.
• Empirical analysis and prototype system design using MATLAB software.
• Validation through case studies and evaluation of implementation personnel.

Main Results:

• The nonlinear hierarchical optimization neural network model demonstrated feasibility and accuracy in talent evaluation.
• Achieved a comprehensive prediction accuracy rate of 88.5% for T-1 year and 83.45% for T-2 year.
• Effectively promoted the process of enterprise talent evaluation.

Conclusions:

• The nonlinear hierarchical optimization neural network is a viable and accurate tool for enterprise talent assessment.
• The proposed model significantly enhances the prediction accuracy of talent evaluation.
• This approach offers a promising solution for overcoming the limitations of traditional talent assessment methods.