Human Resource Management Model Innovation of Forestry Science and Technology Enterprises in the Era of Big Data

Abstract

The competitive advantage based on the practice of human resource management is the key for
high-tech forestry enterprises to survive in a highly uncertain environment. This paper studies
the innovation of human resource management model of science and technology enterprises in
the era of big data. In order to improve the accuracy of human resource cost evaluation, this
paper designs a data-driven human resource cost evaluation algorithm. This paper collects the
data of human resource cost evaluation, and uses chaos theory to reconstruct the data to restore
the characteristics of human resource cost changes. This paper uses extreme learning machine to
establish human resource cost evaluation algorithm, and uses particle swarm optimization
algorithm to optimize extreme learning machine. Finally, the simulation experiment of human
resource cost evaluation is carried out. The results show that the algorithm can reflect the
changing characteristics of human resource cost. This method improves the evaluation results of
human resource cost and achieves better results than other human resource cost evaluation
models. This method provides a theoretical reference for the research of data-driven human
resource optimal allocation model and algorithm.