Multi-Dimensional Post Competency Evaluation Model in Human Resource Management under the Background of Artificial Intelligence

Abstract

In order to evaluate the competence of candidates in human resource management and select the most suitable talents, the behavior time interview method is used to build a personnel competency model, and various competency indicators are determined. According to the existing mature traditional analysis methods, we calculate the weight of each index and give the competency score. Based on the traditional competency model, the parameters of the training content of BP neural network are obtained. After the training results are tested, a new competency evaluation model based on artificial intelligence is proposed. The results show that the relative error between the model training results and the expected output is very small, the maximum value is −0.12%, and the maximum relative error between the output value obtained by BP neural network and the expected value is 3.8%. Therefore, the personnel competency model based on BP neural network constructed in this paper has accurate calculation results, and its application in the company’s human resource management is feasible and has strong applicability.