Design of performance assessment and management model for regional technological innovation under the background of machine learning

This study addresses the limitations and primitiveness of performance evaluation management in regional technological innovation enterprises. We propose a methodology based on the random forest algorithm to overcome these issues. The methodology involves preprocessing raw data from a technology company's project management system through data cleaning, feature selection, and feature transformation. Using the ID3 algorithm, we construct an index weight evaluation model by recursively creating a decision tree and selecting features based on information gain criteria. The refined model generates a performance evaluation total score. Experimental results demonstrate that the random forest algorithm achieves a satisfactory assessment of regional technological innovation performance, with a testing accuracy of 94.20%. These findings establish a scientific foundation for performance evaluation management, enabling enterprises to enhance accuracy and efficiency.