Deconstruction and Implementation of Strategic Human Resource Management Evaluation Algorithm Using Data Mining Technology

Abstract

With the rapid development of information technology, especially the extensive use of databases, computer networks, and other fields, the amount of data held by enterprises is also growing rapidly. With the current shortage of human resources and the pressure of talent competition, the evaluation of human resource management in enterprises is particularly important. Creating measures to attract talents, innovating the understanding management system, formulating a standard talent evaluation system, avoiding the occurrence of improper employment accidents, rationally allocating the weight of human resources, and cultivating the loyalty of employees to the enterprise have become the challenges that more and more companies need to face. In response to the problems raised above, this paper conducts an in-depth excavation of strategic human resource management evaluation and uses in-depth excavation technology. Due to the huge amount of data in the human resource management system, data mining algorithms such as the ID3 algorithm, GBDT algorithm, and Bayesian network are proposed to classify and evaluate the data. Based on these data mining techniques, the strategic human resource management evaluation algorithm is researched and tested. First, the decision tree algorithm is used to build a decision tree for the educational background, identity, and other information of a company’s staff to classify the employees. The factor analysis test results show that after deleting a single similar factor, the variance contribution rate of each influencing human resource management evaluation has a certain increase. Employee engagement and organizational culture rose by 2 percent, while employee satisfaction, organizational learning, and organizational capability each rose by 1 percent. Therefore, data mining technology patient decision tree algorithm is effective for strategic human resource management evaluation.