Evaluation and Image Analysis of Enterprise Human Resource Management Based on the Simulated Annealing‐Optimized BP Neural Network

Abstract

With the continuous development of social economy and the intensification of social competition, human resource management plays a more and more important role in the whole resource system. How to give full play to the advantages of human resources has become the key issue of human resource management evaluation. However, the current human resource management evaluation system has some problems, such as poor timeliness, one-sidedness, and subjectivity. Therefore, this paper proposes a BP image neural network optimized based on the simulated annealing algorithm to realize enterprise human resource management evaluation and image analysis. Through the learning of different time series samples, the average weight distribution scheme of main indicators is obtained, in which the average weight proportions of c1, c2, c3, and c4 are 25.5%, 24.8%, 17.9%, and 31.9%, respectively. In the comprehensive evaluation of enterprise employees, the error between the actual output and expected output is less than 4.5%. The results show that the BP image neural network based on simulated annealing algorithm has high accuracy in the image analysis and evaluation of enterprise human resource management. The output analysis results meet the actual needs of the enterprise and the personal development of employees and provide a decision-making scheme for the evaluation of enterprise human resource management.

Mobile Edge Computing Application in Enterprise Human Resource Management Platform Based on Task Scheduling Algorithm

“…e selection and training within the enterprise improve the skills and quality of the existing employees of the company, so as to achieve the goal of further developing the employee structure. Although this type of management cost is low, the speed is slow, and there may be a significant delay, which requires enterprise decision-makers to make strategic decisions [18,19].…”