Analysis of employee diligence and mining of behavioral patterns based on portrait portrayal

Abstract

With the deepening of enterprise digital construction, the portrait portrayal based on employee behaviors has gradually become a research focus. Currently, the employee's portrait portrayal mostly has the problems of simple means, low efficiency, limited solving ability, etc., making the results more one-sided. Therefore, a data mining-based employee portrait portrayal model is proposed. The content of employee portrait portrayal is deeply analyzed, and the overall framework of the model is designed. A diligence analysis model (DAM) based on improved GAN is constructed, and the diligence evaluation of employees is clarified to realize the diligence evaluation. The results of diligence analysis of DAM have high accuracy (80.39%) and outperform SA (70.24%), K-means (51.79%) and GAN (67.25%). The Kappa coefficient of DAM reaches 0.7384, which is highly consistent and higher than SA (0.6075), K-means (0.3711) and GAN (0.5661). The Local Outlier Factor (LOF) and Isolation Forest (IF) are used to detect abnormal behaviors on the employees, and mine the abnormal behavior patterns on different granularity time. The LSTM model (Att-LSTM) based on the attention mechanism is used to complete the prediction of employees' software usage behaviors, and analyze and summarize the characteristics of employee's behaviors from multiple perspectives. Att-LSTM predicts the best with an RMSE of 0.82983, which is better than LSTM (0.90833) and SA (0.97767); AM-LSTM has a MAPE of 0.80323, which is better than LSTM (0.86233) and SA (0.92223). The results show that the data mining-based employee portrait portrayal method can better solve the problem of enterprise employees' digital construction, and provide a new way of thinking for the construction of enterprise-level employees' digital portrait model and the analysis of employee behavior.