Simulation of Enterprise Human Resource Management Model Based on Long Short-Term Memory (LSTM)

Abstract

The survival and development of enterprises depend on effective human resources, especially in the context of the increasing demand for simplified, scientific, and automated management in modern enterprises. This article explores the application of Long Short Term Memory (LSTM) algorithm in contemporary human resource (HR) management to address challenges such as low efficiency and mismatch between personnel and demand. By utilizing the sequence modeling ability of LSTM networks, the model can better capture the temporal characteristics and correlation relationships of HR data, thereby improving the accuracy and reliability of decision-making. The research results indicate that the LSTM algorithm has the smallest model error when the number of iterations is 11. Using LSTM algorithm in a certain time series can effectively predict the HR required by enterprises, achieve refined and automated HR management, thereby making correct decisions for the development of enterprises and improving work efficiency. The experimental results prove that LSTM can provide correct reference opinions for enterprise decision-making and has a good promoting effect on the development of enterprises.