Research on Human Resource Management and Predictive Models Based on Deep Learning Algorithms

The management and prediction models is critical in human resource management, however it has an issue with erroneous performance positioning. The typical Neural network algorithms is unable to address the resource management issue in human resource management, and the result is insufficient. As a result, a Neural network algorithm-based research on human resource management and predictive models is provided, and the research on human resource management and predictive models is assessed. To begin, the gradient descent theory is used to discover the influencing elements, and the indicators are split based on the management and prediction model's needs to decrease interference factors in the management and prediction models. The gradient descent theory is then used to create a Neural network algorithm management and prediction models scheme, and the outcomes of the management and prediction models are thoroughly examined. The MATLAB simulation results reveal that, under particular evaluation conditions, the Neural network algorithm outperforms the standard Neural network algorithms in terms of management and prediction models accuracy and time of influencing variables. Based on the RBF neural network, the human resource demand prediction model is established, and a large amount of disordered data are trained, learned and tested, and finally the rules of the enterprise's employment demand provide a more informative basis for the enterprise to put forward the correct strategy, which has a greater practical value.