Design and simulation of excellent digital economy talent profiling system based on machine learning algorithm

The system designed in this paper uses machine learning algorithms to construct the portrait of excellent talents in digital economy, and the system includes steps such as data collection, data preprocessing, feature selection, model training and model evaluation. First, the system collects relevant personal and professional information data, including education, work experience, skills, etc., through web crawlers and API interfaces. Next, the system uses a feature selection algorithm to select features from a large number of features that have an important impact on digital economy excellence talent. Finally, the system assesses the quality and effectiveness of the model by evaluating its performance metrics, such as accuracy, recall, and F1 value. In order to verify the effectiveness of the system, this paper uses Python programming code to conduct simulation experiments. The experimental results show that the system is able to predict the outstanding talents of the digital economy more accurately and identify the key features that have an important impact on the development of the digital economy. These results validate the feasibility and effectiveness of the system, and provide an effective method for enterprises and organisations to identify and cultivate excellent talents in the field of digital economy.