Design And Platform Implementation Of Employee Turnover Warning Algorithm Based On Deep Learning

The predictive employee turnover algorithm is an important area of research that combines human resource management and data mining.It aims to analyze employee behavior and predict the risk of employee attrition.Employee turnover is a normal personnel change for companies.However,high turnover rates can lead to costly training expenses,interruptions in product lines,and the loss of trade secrets,which can also affect employee morale and company trust.Therefore,early identification of employee attrition intentions allows for targeted retention efforts to avoid the aforementioned risks.The use of predictive employee turnover algorithms to forecast the probability of employee attrition holds significant value.Traditional employee turnover prediction methods analyze static datasets by aggregating an employee’s past behavior into a single sample for analysis.This approach fails to consider the impact of time on changes in employee behavior,.resulting in the inability to provide realtime warnings and overlooking the importance of other modal data,such as textual data.This paper addresses this limitation by introducing deep learning techniques,which have strong capabilities in handling time series data and natural language text data.Mature pre-training and fine-tuning techniques,as well as online model training,allow deep learning models to continuously learn autonomously.With increasing amounts of processed data,their performance also improves.This paper innovatively processes employee behavior data into a time series format and constructs an employee weekly report dataset,aiming to utilize deep learning techniques to extract richer information and achieve better results than traditional methods.Furthermore,this paper designs a mechanism for automatic model training,enabling the model to iterate and enhance its performance while handling business data,providing more tailored technical support for companies.To achieve this goal,this paper first proposes a real-time employee attrition prediction algorithm based on deep learning technology.This algorithm includes a weekly report sentiment analysis module and an employee turnover predictor module.The weekly report sentiment analysis module analyzes the sentiment tendency of employee weekly reports to determine employee job satisfaction,providing support for employee attrition prediction.The employee turnover predictor analyzes the employee’s behavioral characteristics over time to predict their departure probability.Then,the paper verifies the effectiveness of the two modules through experiments.Next,the paper divides the dataset into time intervals and fine-tunes and tests the model according to the time scale.It was found that the model’s performance also continuously improves over time,demonstrating the effectiveness of continuous learning.Furthermore,the paper further deploys the model online,enabling it to create value in real enterprise production scenarios.Finally,this paper develops an attrition prediction system that not only displays the algorithm’s results but also provides services such as an analysis of the factors affecting attrition,visualization of attrition conditions,and analysis of attrition trends,providing more powerful support for managers.The paper follows the software engineering development process,conducting requirement analysis,module analysis,and interface analysis during the system development phase,and testing and demonstrating the effectiveness of the designed system.