Analyzing and applying research of the employee value assessment model based on deep work

The application of array-driven neural network modeling is currently one of the hot topics of interdisciplinary research, especially in terms of employee performance prediction. This application extracts and explores key information in the form of a job training array to help job trainers accurately assess the work status of employees. In view of the current lack of accuracy of measurement and evaluation models, this paper proposes an innovative width-of-work employee value network model that incorporates DA-GRU-CNN technology. This model consists of three modules: an array processing module, a network DA-GRU-CNN measurement module, and a performance prediction module. After practical testing and verification, the DA-GRU-CNN model proposed in this paper has been shown to have higher accuracy and greater adaptability in determining the feedback time, with an average accuracy of up to 98.5% on width work. The performance prediction module relies on a proven high-quality network model to quickly identify and accurately predict employee value.