Design and Simulation of Human Resource Allocation Model Based on Double-Cycle Neural Network.

Area of Science:

• Human Resource Management
• Data Science
• Artificial Intelligence

Background:

• Traditional human resource management relies on statistical computation, which struggles with massive datasets and hidden data characteristics, leading to information scarcity.
• Existing computational models in human resources are insufficient for processing big data and identifying nuanced patterns, impacting talent management efficiency.

Purpose of the Study:

• To develop an advanced job matching recommendation algorithm for human resource management.
• To address the limitations of traditional statistical models in handling large-scale, complex human resource data.
• To improve the accuracy and efficiency of talent training and resource utilization in enterprises.

Main Methods:

• Integration of recurrent convolutional neural networks with traditional human resource allocation algorithms.
• Design and implementation of a double recurrent neural network (DRNN) for job matching and recommendations.
• Comparative analysis of the proposed DRNN algorithm against existing methods using key performance metrics.

Main Results:

• The DRNN algorithm demonstrated a significant improvement in data training quality by enhancing hidden layer features.
• Achieved an arithmetic F1 score of 0.823, outperforming other algorithms by 20.1% and 7.4%.
• The proposed algorithm shows superior performance in job matching and recommendation accuracy.

Conclusions:

• The double recurrent neural network algorithm offers a robust solution for modern human resource management challenges.
• Enhanced data processing capabilities lead to improved talent allocation and enterprise resource utilization.
• The findings suggest a promising direction for leveraging deep learning in optimizing human resource functions.