Optimization of Performance Management for Commercial Companies by Integrating ROF and Light GBM Algorithms

Performance management in commercial companies is related to the operation and development of enterprises, and as a core competitiveness of enterprises, it can promote the achievement of strategic goals. Research and analyze the key performance indicator method, target management method, balanced scorecard, and 360 degree performance evaluation method, combined with the current situation and future development direction of performance management in commercial companies, and establish performance evaluation indicators and standards based on enterprise goals to promote the achievement of enterprise strategic goals. After comparing the datasets of random forest model, rotating forest model, and classification model based on rotating forest and Light GBM, it was found that the ROF -LGB model accounted for 75% and 66.7% of the micro average gap and macro average gap, respectively, with an accuracy of 82.6%. It has been proven that the classification model based on rotating forest and Light GBM has high accuracy and stability.