Improved Back-Propagation Neural Network Applied to Enterprise Employee Performance Appraisal and Evaluation

Abstract

Evaluating employee performance in enterprises is beneficial for improving talent competitiveness, operation level production, and overall enterprise performance. At present, many performance appraisal methods lack objectivity and fairness, which is not conducive to the long-term development of enterprises. Therefore, more research into scientific and effective performance appraisal methods is required. This study takes the grassroots employees of Enterprise A as an example, improving the existing performance appraisal evaluation indices to evaluate employee performance from three dimensions, including achievement, ability, and attitude, as determined by index weights using the analytic hierarchy process approach. An improved back-propagation neural network (BPNN) method is then designed to obtain performance appraisal and evaluation results. The error between the output of the improved BPNN method and expected output was small. Of the 20 extracted samples, the maximum and minimum error values were 0.05 and 0.01, respectively, and the average error was 0.03. The improved BPNN method evaluated only one out of 20 samples incorrectly, and the accuracy of the improved BPNN method was 96.21%, which is 19.88% and 10.75% higher than those of support vector machine and standard BPNN, respectively. The findings demonstrate that the improved BPNN method can be used in the appraisal and assessment of enterprise employee performance and has practical application value.