Employee Competency Assessment Optimisation Algorithm Based on Artificial Intelligence Generative Adversarial Network

With the growing demand for employee competency assessment in enterprises, artificial intelligence technology has gradually become an important tool. Most of the current employee competency assessments use models such as LSTM, which cannot adapt to data scarcity or bias, resulting in poor accuracy of competency assessment. This paper takes advantage of GAN’s adversarial data expansion to optimize the LSTM employee competency assessment model to address the insufficient data volume and uneven data distribution. The study first uses an MLP to build a generator and a DNN to build a discriminator, and jointly designs the GAN structure. Then, GAN is used to generate samples that are similar to, yet distinct from, the original data. The generated samples use adversarial training to approach the original data scatter gradually. Finally, the original data and synthetic data (i.e., GAN-generated data) are sent to the LSTM model for training to capture the time series dependency of employee capability data. The experimental results show that when the LSTM model is used in conjunction with the original data and the synthetic data, the MAE is only 0.12, which is 0.03 lower than the MAE of the original data. The KL divergence of the quality of the GAN synthetic data is only 0.29. The experimental results show that the use of artificial intelligence to generate adversarial networks can significantly optimize the performance of employee ability assessment, reduce assessment errors, and promote the improvement of the company’s competitiveness.