Research on Innovative Application of Machine Learning Algorithm in Performance Evaluation System of Artificial Intelligence

This paper discusses the design of a machine learning algorithm based on multi-indicator decision making to improve the accuracy and efficiency of performance evaluation. Firstly, an ensemble learning framework is proposed to solve the inherent complexity and uncertainty in performance evaluation by integrating multiple machine learning models. The framework is capable of processing large volumes of heterogeneous data while considering multiple metrics, including but not limited to sales performance, team collaboration, customer satisfaction, and innovation. In order to verify the effectiveness of the proposed algorithm, a set of comprehensive model simulation experiments are designed in this paper. The experiments were conducted on multiple real-world data sets, covering different industry and organization sizes. Simulation results show that compared with traditional evaluation methods, the proposed algorithm can significantly improve the accuracy and fairness of evaluation, and reduce the subjective bias in the evaluation process. In addition, through comparative analysis, it is found that ensemble learning method has better generalization ability and robustness when dealing with large-scale data. The research in this paper not only provides a theoretical basis for the innovation of performance evaluation system, but also provides practical guidance for organizations to implement a more intelligent and objective evaluation process in practical applications.